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Official task code, descriptions and episodes are credited to the EmbodiedSWE authors. + +The upstream code LICENSE is Apache-2.0 and is retained verbatim in LICENSE. Some official HF datasets declare Apache-2.0; others do not declare a dataset-specific license in their card metadata. Per-source metadata is preserved in provenance/tasks.json. Website clips are credited and linked to their original project-page files. This curation does not create additional rights or replace upstream terms. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ca3f46457cc4d14e19bd147bddd8b417c1653d46 --- /dev/null +++ b/README.md @@ -0,0 +1,1075 @@ +--- +language: +- en +- zh +license: other +license_name: upstream-embodiedswe-terms +license_link: https://huggingface.co/datasets/Travor278/EmbodiedSWE-Task-Examples/blob/main/NOTICE.md +pretty_name: EmbodiedSWE Simulation Task Examples +tags: +- robotics +- video +- simulation +configs: +- config_name: all + data_files: + - split: examples + path: data/all.parquet + default: true +- config_name: scene_allen_bolt + data_files: + - split: examples + path: data/scene_allen_bolt.parquet +- config_name: scene_bulb + data_files: + - split: examples + path: data/scene_bulb.parquet +- config_name: scene_ikea_table + data_files: + - split: examples + path: data/scene_ikea_table.parquet +- config_name: scene_so101 + data_files: + - split: examples + path: data/scene_so101.parquet +- config_name: scene_nut_thread + data_files: + - split: examples + path: data/scene_nut_thread.parquet +- config_name: scene_pc_gpu + data_files: + - split: examples + path: data/scene_pc_gpu.parquet +- config_name: scene_pc_gpu_ram + data_files: + - split: examples + path: data/scene_pc_gpu_ram.parquet +- config_name: scene_pc_motherboard + data_files: + - split: examples + path: data/scene_pc_motherboard.parquet +- config_name: scene_pc_ram + data_files: + - split: examples + path: data/scene_pc_ram.parquet +- config_name: scene_pen_holder + data_files: + - split: examples + path: data/scene_pen_holder.parquet +- config_name: scene_tool_packing + data_files: + - split: examples + path: data/scene_tool_packing.parquet +- config_name: scene_egg_carton + data_files: + - split: examples + path: data/scene_egg_carton.parquet +- config_name: scene_clear_organic_objects + data_files: + - split: examples + path: data/scene_clear_organic_objects.parquet +- config_name: scene_coffee + data_files: + - split: examples + path: data/scene_coffee.parquet +- config_name: scene_spatula + data_files: + - split: examples + path: data/scene_spatula.parquet +- config_name: scene_syringe + data_files: + - split: examples + path: data/scene_syringe.parquet +- config_name: scene_push_shapes + data_files: + - split: examples + path: data/scene_push_shapes.parquet +- config_name: scene_classify_objects + data_files: + - split: examples + path: data/scene_classify_objects.parquet +- config_name: scene_stack_blocks + data_files: + - split: examples + path: data/scene_stack_blocks.parquet +- config_name: scene_tshirt + data_files: + - split: examples + path: data/scene_tshirt.parquet +- config_name: scene_latte + data_files: + - split: examples + path: data/scene_latte.parquet +- config_name: scene_dumpling + data_files: + - split: examples + path: data/scene_dumpling.parquet +- config_name: scene_shoe_knot + data_files: + - split: examples + path: data/scene_shoe_knot.parquet +- config_name: scene_slice + data_files: + - split: examples + path: data/scene_slice.parquet +- config_name: scene_dice + data_files: + - split: examples + path: data/scene_dice.parquet +- config_name: scene_fruit_delivery + data_files: + - split: examples + path: data/scene_fruit_delivery.parquet +- config_name: scene_box_to_bin + data_files: + - split: examples + path: data/scene_box_to_bin.parquet +- config_name: scene_wheel_carry + data_files: + - split: examples + path: data/scene_wheel_carry.parquet +- config_name: assembly + data_files: + - split: examples + path: data/assembly.parquet +- config_name: cutting + data_files: + - split: examples + path: data/cutting.parquet +- config_name: deformable + data_files: + - split: examples + path: data/deformable.parquet +- config_name: liquid + data_files: + - split: examples + path: data/liquid.parquet +- config_name: locomanip + data_files: + - split: examples + path: data/locomanip.parquet +- config_name: organizing + data_files: + - split: examples + path: data/organizing.parquet +- config_name: puzzle + data_files: + - split: examples + path: data/puzzle.parquet +--- +# EmbodiedSWE Simulation Task Examples + +[打开任务 MP4 浏览页](https://travor278-embodiedswe-task-examples.static.hf.space/?task=bulb) + +28 个官网场景 · 26 段官网 MP4 · 10 个官方数据来源各 3 条完整 episode，共 30 条多相机示教。shoe_knot 与 dice 暂无官方展示视频，保留任务目录和评分标准。 + +**序号规则：** 本页 C01–C28 是按官网目录顺序生成的辅助编号，官方任务以 `suite.scene` 标识。LeRobot `task_index` 和 `episode_index` 都只在各自 `source_repo` 内有效，绝不能把各库的 task_index=0 当成同一任务。 + +**相机：** 按官方实际提供的 front/front_wide/high/side/corner/wrist 展示，单臂 wrist 不伪装成 left/right。官网演示只展示其原有视角。 + +**评分：** 来自当前公开代码的 BaseGrader 与具体 RUBRIC，progress 是归一化权重和。live=当前值；once=历史最好；final=交付末端才评估。历史数据生成使用的 grader 可能不同；示例没有公开逐条分数，因此 actual_episode_score/success 留空。 + +[Franka 腕部相机说明](https://travor278-embodiedswe-task-examples.static.hf.space/?task=camera-guide) · [官方任务页](https://embodiedswe.github.io/#benchmark) + +| 辅助目录号 | 官方 scene ID | Task | Category | 直接浏览 | +|---|---|---|---|---| +| C01 | `assembly.allen_bolt` | Allen bolt | Assembly | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=allen_bolt) | +| C02 | `assembly.bulb` | Bulb screwing | Assembly | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=bulb) | +| C03 | `assembly.ikea_table` | Table assembly | Assembly | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=ikea_table) | +| C04 | `assembly.so101` | Robot-arm assembly | Assembly | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=so101) | +| C05 | `assembly.nut_thread` | Nut threading | Assembly | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=nut_thread) | +| C06 | `assembly.pc_gpu` | GPU insertion | Assembly | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=pc_gpu) | +| C07 | `assembly.pc_gpu_ram` | GPU + RAM install | Assembly | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=pc_gpu_ram) | +| C08 | `assembly.pc_motherboard` | Motherboard mount | Assembly | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=pc_motherboard) | +| C09 | `assembly.pc_ram` | RAM seating | Assembly | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=pc_ram) | +| C10 | `packing.pen_holder` | Pen holder | Organizing | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=pen_holder) | +| C11 | `packing.tool_packing` | Tool packing | Organizing | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=tool_packing) | +| C12 | `packing.egg_carton` | Egg carton | Organizing | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=egg_carton) | +| C13 | `packing.clear_organic_objects` | Clear organics | Organizing | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=clear_organic_objects) | +| C14 | `puzzle.coffee` | Coffee brewing | Puzzle | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=coffee) | +| C15 | `puzzle.spatula` | Spatula flip | Puzzle | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=spatula) | +| C16 | `puzzle.syringe` | Syringe dosing | Liquid | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=syringe) | +| C17 | `puzzle.push_shapes` | Push shapes | Puzzle | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=push_shapes) | +| C18 | `puzzle.classify_objects` | Classify objects | Puzzle | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=classify_objects) | +| C19 | `puzzle.stack_blocks` | Stack blocks | Puzzle | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=stack_blocks) | +| C20 | `deformable.tshirt` | T-shirt folding | Deformable | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=tshirt) | +| C21 | `deformable.latte` | Latte pouring | Liquid | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=latte) | +| C22 | `deformable.dumpling` | Dough rolling | Deformable | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=dumpling) | +| C23 | `deformable.shoe_knot` | Shoelace half-knot | Deformable | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=shoe_knot) | +| C24 | `cutting.slice` | Banana slicing | Cutting | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=slice) | +| C25 | `cutting.dice` | Food dicing | Cutting | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=dice) | +| C26 | `locomanip.fruit_delivery` | Fruit delivery | Locomanip | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=fruit_delivery) | +| C27 | `locomanip.box_to_bin` | Box to bin | Locomanip | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=box_to_bin) | +| C28 | `locomanip.wheel_carry` | Wheel carry | Locomanip | [打开](https://travor278-embodiedswe-task-examples.static.hf.space/?task=wheel_carry) | + +## C01 · Allen bolt · assembly.allen_bolt + +**Instruction / objective:** Drive an Allen bolt into a threaded plate using an L-shaped key. + +**Difficulty:** Medium · **官网列出的 evaluated on:** Franka / xArm7 / Gen3 + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=allen_bolt) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/assembly/grader/allen_bolt_assembly.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| key_grasped | 1 | 50.00% | once | +| driven | 1 | 50.00% | live | + +```python +def check_success(self): + return self.scene.success() +``` + +[官网展示 MP4](media/allen_bolt/website_demo.mp4) · 网站原始播放倍率 1.0x；该示例不等同于公开三相机训练轨迹。 + +## C02 · Bulb screwing · assembly.bulb + +**Instruction / objective:** Pick up a light bulb and screw it into its socket until fully seated. + +**Difficulty:** Medium · **官网列出的 evaluated on:** Franka / xArm7 / Gen3 + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=bulb) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/assembly/grader/bulb_assembly.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| lifted | 1 | 33.33% | once | +| engaged | 1 | 33.33% | live | +| threaded | 1 | 33.33% | live | + +```python +def check_success(self): + return self.scene.seated().all(dim=1) +``` + +**Official dataset:** [EmbodiedSWE/bulb_franka_pinkik.jointtarget_15hz_generalization_train_20260921](https://huggingface.co/datasets/EmbodiedSWE/bulb_franka_pinkik.jointtarget_15hz_generalization_train_20260921) · 15 FPS · cameras: front, wrist + +**source task_index=0 / episode_index=0** · 2731 frames / 15 FPS / 182.07 s + +**Dataset instruction:** pick up the bulb, set it on the socket, and screw it down (turn it clockwise while pressing down) until it seats. A seated bulb is held by its thread, and GLOWS ever brighter as it screws home — from a faint orange at first electrical contact to fully bright warm white exactly when seated. The task is complete once the bulb is seated. + +![Source camera composite](media/bulb/episode_000000/preview.jpg) + +[同步 MP4](media/bulb/episode_000000/synchronized.mp4) · [front MP4](media/bulb/episode_000000/front.mp4) · [wrist MP4](media/bulb/episode_000000/wrist.mp4) · [官方轨迹 Parquet](trajectories/bulb/episode_000000.parquet) + +**source task_index=0 / episode_index=408** · 1317 frames / 15 FPS / 87.80 s + +**Dataset instruction:** pick up the bulb, set it on the socket, and screw it down (turn it clockwise while pressing down) until it seats. A seated bulb is held by its thread, and GLOWS ever brighter as it screws home — from a faint orange at first electrical contact to fully bright warm white exactly when seated. The task is complete once the bulb is seated. + +![Source camera composite](media/bulb/episode_000408/preview.jpg) + +[同步 MP4](media/bulb/episode_000408/synchronized.mp4) · [front MP4](media/bulb/episode_000408/front.mp4) · [wrist MP4](media/bulb/episode_000408/wrist.mp4) · [官方轨迹 Parquet](trajectories/bulb/episode_000408.parquet) + +**source task_index=0 / episode_index=815** · 1745 frames / 15 FPS / 116.33 s + +**Dataset instruction:** pick up the bulb, set it on the socket, and screw it down (turn it clockwise while pressing down) until it seats. A seated bulb is held by its thread, and GLOWS ever brighter as it screws home — from a faint orange at first electrical contact to fully bright warm white exactly when seated. The task is complete once the bulb is seated. + +![Source camera composite](media/bulb/episode_000815/preview.jpg) + +[同步 MP4](media/bulb/episode_000815/synchronized.mp4) · [front MP4](media/bulb/episode_000815/front.mp4) · [wrist MP4](media/bulb/episode_000815/wrist.mp4) · [官方轨迹 Parquet](trajectories/bulb/episode_000815.parquet) + +[官网展示 MP4](media/bulb/website_demo.mp4) · 网站原始播放倍率 5.0x；该示例不等同于公开三相机训练轨迹。 + +## C03 · Table assembly · assembly.ikea_table + +**Instruction / objective:** Attach four threaded legs to the corner studs of a tabletop. + +**Difficulty:** Hard · **官网列出的 evaluated on:** Bimanual Franka + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=ikea_table) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/assembly/grader/ikea_table_assembly.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| aligned | 1 | 50.00% | live | +| seated | 1 | 50.00% | live | + +```python +def check_success(self): + return self.scene.success() +``` + +[官网展示 MP4](media/ikea_table/website_demo.mp4) · 网站原始播放倍率 4.0x；该示例不等同于公开三相机训练轨迹。 + +## C04 · Robot-arm assembly · assembly.so101 + +**Instruction / objective:** Assemble a robot arm. + +**Difficulty:** Hard · **官网列出的 evaluated on:** Bimanual Franka + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=so101) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/assembly/grader/so101_assembly.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| motor_seated | 1 | 7.14% | live | +| tab_screws_picked | 2 | 14.29% | once | +| tab_screws_fastened | 2 | 14.29% | live | +| fork_seated | 1 | 7.14% | live | +| horn_screws_picked | 4 | 28.57% | once | +| horn_screws_fastened | 4 | 28.57% | live | + +```python +def check_success(self): + """Every loose screw fastened (the scene's `state == 2`) AND the servo live in the + pocket AND the forearm live on the horn — the assembled elbow, on the final state.""" + all_fastened = (self.scene.fastened >= 0).all(dim=1) + return all_fastened & self._motor_seated() & self._fork_seated() +``` + +[官网展示 MP4](media/so101/website_demo.mp4) · 网站原始播放倍率 1.0x；该示例不等同于公开三相机训练轨迹。 + +## C05 · Nut threading · assembly.nut_thread + +**Instruction / objective:** Pick up an M16 nut and thread it onto a fixed bolt. + +**Difficulty:** Easy · **官网列出的 evaluated on:** Franka / xArm7 / Gen3 + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=nut_thread) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/assembly/grader/nut_thread_assembly.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| lifted | 1 | 33.33% | once | +| aligned | 1 | 33.33% | live | +| threaded | 1 | 33.33% | live | + +```python +def check_success(self): + return self.scene.seated().all(dim=1) +``` + +**Official dataset:** [EmbodiedSWE/nut_thread_franka_ik.jointtarget_20hz_20260905](https://huggingface.co/datasets/EmbodiedSWE/nut_thread_franka_ik.jointtarget_20hz_20260905) · 20 FPS · cameras: front, side, wrist + +**source task_index=0 / episode_index=0** · 3661 frames / 20 FPS / 183.05 s + +**Dataset instruction:** pick up the nut, set it on the bolt, and screw it down (turn it clockwise while pressing down) until it seats. A seated nut locks in place. The task is complete once the nut is seated. + +![Source camera composite](media/nut_thread/episode_000000/preview.jpg) + +[同步 MP4](media/nut_thread/episode_000000/synchronized.mp4) · [front MP4](media/nut_thread/episode_000000/front.mp4) · [side MP4](media/nut_thread/episode_000000/side.mp4) · [wrist MP4](media/nut_thread/episode_000000/wrist.mp4) · [官方轨迹 Parquet](trajectories/nut_thread/episode_000000.parquet) + +**source task_index=0 / episode_index=200** · 2298 frames / 20 FPS / 114.90 s + +**Dataset instruction:** pick up the nut, set it on the bolt, and screw it down (turn it clockwise while pressing down) until it seats. A seated nut locks in place. The task is complete once the nut is seated. + +![Source camera composite](media/nut_thread/episode_000200/preview.jpg) + +[同步 MP4](media/nut_thread/episode_000200/synchronized.mp4) · [front MP4](media/nut_thread/episode_000200/front.mp4) · [side MP4](media/nut_thread/episode_000200/side.mp4) · [wrist MP4](media/nut_thread/episode_000200/wrist.mp4) · [官方轨迹 Parquet](trajectories/nut_thread/episode_000200.parquet) + +**source task_index=0 / episode_index=400** · 2181 frames / 20 FPS / 109.05 s + +**Dataset instruction:** pick up the nut, set it on the bolt, and screw it down (turn it clockwise while pressing down) until it seats. A seated nut locks in place. The task is complete once the nut is seated. + +![Source camera composite](media/nut_thread/episode_000400/preview.jpg) + +[同步 MP4](media/nut_thread/episode_000400/synchronized.mp4) · [front MP4](media/nut_thread/episode_000400/front.mp4) · [side MP4](media/nut_thread/episode_000400/side.mp4) · [wrist MP4](media/nut_thread/episode_000400/wrist.mp4) · [官方轨迹 Parquet](trajectories/nut_thread/episode_000400.parquet) + +[官网展示 MP4](media/nut_thread/website_demo.mp4) · 网站原始播放倍率 1.0x；该示例不等同于公开三相机训练轨迹。 + +## C06 · GPU insertion · assembly.pc_gpu + +**Instruction / objective:** Align a graphics card with a PCIe slot and press it into place. + +**Difficulty:** Easy · **官网列出的 evaluated on:** Franka / xArm7 / Gen3 + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=pc_gpu) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/assembly/grader/pc_gpu_assembly.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| grasped | 1 | 33.33% | once | +| aligned | 1 | 33.33% | live | +| pressed | 1 | 33.33% | live | + +```python +def check_success(self): + return self.scene.seated() +``` + +**Official dataset:** [EmbodiedSWE/pc_gpu_franka_osc.jointpos_15hz_20260907](https://huggingface.co/datasets/EmbodiedSWE/pc_gpu_franka_osc.jointpos_15hz_20260907) · 15 FPS · cameras: front, wrist + +**source task_index=0 / episode_index=0** · 2010 frames / 15 FPS / 134.00 s + +**Dataset instruction:** grip the card by its top edge, lift it straight out of the holder, lower it into the case with its I/O bracket just forward of the rear panel, slide it rearward until the bracket and ports pass through the cutout, line its PCB edge connector up with the x16 slot, and press it straight down until it bottoms out. A seated card stays put on its own. The task is complete once the card is fully seated. The card holds in a firm pinch: close the fingers squarely across its body slab near the top edge and the grip locks; open wide to release. + +![Source camera composite](media/pc_gpu/episode_000000/preview.jpg) + +[同步 MP4](media/pc_gpu/episode_000000/synchronized.mp4) · [front MP4](media/pc_gpu/episode_000000/front.mp4) · [wrist MP4](media/pc_gpu/episode_000000/wrist.mp4) · [官方轨迹 Parquet](trajectories/pc_gpu/episode_000000.parquet) + +**source task_index=2 / episode_index=49** · 1350 frames / 15 FPS / 90.00 s + +**Dataset instruction:** grip the card by its top edge, lift it straight out of the holder, lower it into the case with its I/O bracket just forward of the rear panel, slide it rearward until the bracket and ports pass through the cutout, line its PCB edge connector up with the x16 slot, and press it straight down until it bottoms out. A seated card stays put on its own. The task is complete once the card is fully seated. The card holds in a firm pinch: close the fingers squarely across its body slab near the top edge and the grip locks; open wide to release. +The case has been set down by hand and is not on a fixed mark: its position on the bench shifts by a centimetre or so from one build to the next, so the slot's location must be read off the case itself rather than assumed. Its orientation is always the same — square to the bench. + +![Source camera composite](media/pc_gpu/episode_000049/preview.jpg) + +[同步 MP4](media/pc_gpu/episode_000049/synchronized.mp4) · [front MP4](media/pc_gpu/episode_000049/front.mp4) · [wrist MP4](media/pc_gpu/episode_000049/wrist.mp4) · [官方轨迹 Parquet](trajectories/pc_gpu/episode_000049.parquet) + +**source task_index=4 / episode_index=114** · 1830 frames / 15 FPS / 122.00 s + +**Dataset instruction:** grip the card by its top edge, lift it straight out of the holder, lower it into the case with its I/O bracket just forward of the rear panel, slide it rearward until the bracket and ports pass through the cutout, line its PCB edge connector up with the x16 slot, and press it straight down until it bottoms out. A seated card stays put on its own. The task is complete once the card is fully seated. The card holds in a firm pinch: close the fingers squarely across its body slab near the top edge and the grip locks; open wide to release. +The work is raised: the case stands on a grey 60 mm bench riser and the card's holder on a matching plinth of the same height, so the whole job — the slot, the card and the space above them — sits that much higher off the table top than the bare bench would put it. The two platforms are fixed bench furniture: they do not move and are not part of the task. + +![Source camera composite](media/pc_gpu/episode_000114/preview.jpg) + +[同步 MP4](media/pc_gpu/episode_000114/synchronized.mp4) · [front MP4](media/pc_gpu/episode_000114/front.mp4) · [wrist MP4](media/pc_gpu/episode_000114/wrist.mp4) · [官方轨迹 Parquet](trajectories/pc_gpu/episode_000114.parquet) + +[官网展示 MP4](media/pc_gpu/website_demo.mp4) · 网站原始播放倍率 1.0x；该示例不等同于公开三相机训练轨迹。 + +## C07 · GPU + RAM install · assembly.pc_gpu_ram + +**Instruction / objective:** Install a graphics card and then seat two memory modules in their slots. + +**Difficulty:** Medium · **官网列出的 evaluated on:** Franka / xArm7 / Gen3 + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=pc_gpu_ram) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/assembly/grader/pc_gpu_ram_assembly.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| gpu_grasped | 1 | 11.11% | once | +| gpu_aligned | 1 | 11.11% | live | +| gpu_seated | 1 | 11.11% | live | +| ram_grasped | 2 | 22.22% | once | +| ram_aligned | 2 | 22.22% | live | +| ram_seated | 2 | 22.22% | live | + +```python +def check_success(self): + return self.scene.success() +``` + +**Official dataset:** [EmbodiedSWE/pc_gpu_ram_franka_osc.jointpos_15hz_20260907](https://huggingface.co/datasets/EmbodiedSWE/pc_gpu_ram_franka_osc.jointpos_15hz_20260907) · 15 FPS · cameras: front, high, wrist + +**source task_index=0 / episode_index=0** · 2850 frames / 15 FPS / 190.00 s + +**Dataset instruction:** install the card first — grip it by its top edge, lift it out of its holder, lower it into the case with its I/O bracket just forward of the rear panel, slide it rearward until the bracket and ports pass through the cutout, line its PCB edge connector up with the x16 slot, and press it straight down until it bottoms out — then install each RAM stick: carry it over the case wall, line its gold edge connector up with its slot (the outermost and the second-from-socket, the alternating pair a dual-channel kit fills), and press it straight down until it clicks fully home. Every seated part stays put on its own. The task is complete once the card and both sticks are fully seated. A part holds in a firm pinch: close the fingers squarely across the card's body slab or a stick's faces, near the top edge, and the grip locks; open wide to release. + +![Source camera composite](media/pc_gpu_ram/episode_000000/preview.jpg) + +[同步 MP4](media/pc_gpu_ram/episode_000000/synchronized.mp4) · [front MP4](media/pc_gpu_ram/episode_000000/front.mp4) · [high MP4](media/pc_gpu_ram/episode_000000/high.mp4) · [wrist MP4](media/pc_gpu_ram/episode_000000/wrist.mp4) · [官方轨迹 Parquet](trajectories/pc_gpu_ram/episode_000000.parquet) + +**source task_index=1 / episode_index=16** · 2070 frames / 15 FPS / 138.00 s + +**Dataset instruction:** install the card first — grip it by its top edge, lift it out of its holder, lower it into the case with its I/O bracket just forward of the rear panel, slide it rearward until the bracket and ports pass through the cutout, line its PCB edge connector up with the x16 slot, and press it straight down until it bottoms out — then install each RAM stick: carry it over the case wall, line its gold edge connector up with its slot (the outermost and the second-from-socket, the alternating pair a dual-channel kit fills), and press it straight down until it clicks fully home. Every seated part stays put on its own. The task is complete once the card and both sticks are fully seated. A part holds in a firm pinch: close the fingers squarely across the card's body slab or a stick's faces, near the top edge, and the grip locks; open wide to release. +The bench is a working one: well to the west of the case, clear of the case, the parts and every path between them, a shallow steel parts tray, a ceramic coffee mug and the motherboard's printed manual lie about. None of them belongs to this job — leave them where they are. + +![Source camera composite](media/pc_gpu_ram/episode_000016/preview.jpg) + +[同步 MP4](media/pc_gpu_ram/episode_000016/synchronized.mp4) · [front MP4](media/pc_gpu_ram/episode_000016/front.mp4) · [high MP4](media/pc_gpu_ram/episode_000016/high.mp4) · [wrist MP4](media/pc_gpu_ram/episode_000016/wrist.mp4) · [官方轨迹 Parquet](trajectories/pc_gpu_ram/episode_000016.parquet) + +**source task_index=2 / episode_index=84** · 2070 frames / 15 FPS / 138.00 s + +**Dataset instruction:** install the card first — grip it by its top edge, lift it out of its holder, lower it into the case with its I/O bracket just forward of the rear panel, slide it rearward until the bracket and ports pass through the cutout, line its PCB edge connector up with the x16 slot, and press it straight down until it bottoms out — then install each RAM stick: carry it over the case wall, line its gold edge connector up with its slot (the outermost and the second-from-socket, the alternating pair a dual-channel kit fills), and press it straight down until it clicks fully home. Every seated part stays put on its own. The task is complete once the card and both sticks are fully seated. A part holds in a firm pinch: close the fingers squarely across the card's body slab or a stick's faces, near the top edge, and the grip locks; open wide to release. +The bench stands in a service bay: a pegboard panel rises along its far edge, a steel shelf is cantilevered above that panel well over head height for the arm, and a wooden crate stands on the floor off the bench's west end. All three are fixed parts of the room — not objects to handle, and none is within reach of the work. + +![Source camera composite](media/pc_gpu_ram/episode_000084/preview.jpg) + +[同步 MP4](media/pc_gpu_ram/episode_000084/synchronized.mp4) · [front MP4](media/pc_gpu_ram/episode_000084/front.mp4) · [high MP4](media/pc_gpu_ram/episode_000084/high.mp4) · [wrist MP4](media/pc_gpu_ram/episode_000084/wrist.mp4) · [官方轨迹 Parquet](trajectories/pc_gpu_ram/episode_000084.parquet) + +[官网展示 MP4](media/pc_gpu_ram/website_demo.mp4) · 网站原始播放倍率 2.0x；该示例不等同于公开三相机训练轨迹。 + +## C08 · Motherboard mount · assembly.pc_motherboard + +**Instruction / objective:** Use an Allen key to drive seven mounting bolts and secure a motherboard inside a case. + +**Difficulty:** Medium · **官网列出的 evaluated on:** Franka / xArm7 / Gen3 + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=pc_motherboard) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/assembly/grader/pc_motherboard_assembly.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| key_grasped | 1 | 12.50% | once | +| driven | 7 | 87.50% | live | + +```python +def check_success(self): + return self.scene.success() +``` + +[官网展示 MP4](media/pc_motherboard/website_demo.mp4) · 网站原始播放倍率 2.0x；该示例不等同于公开三相机训练轨迹。 + +## C09 · RAM seating · assembly.pc_ram + +**Instruction / objective:** Align and seat two memory modules in separate DIMM slots. + +**Difficulty:** Easy · **官网列出的 evaluated on:** Franka / xArm7 / Gen3 + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=pc_ram) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/assembly/grader/pc_ram_assembly.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| grasped | 1 | 33.33% | once | +| aligned | 1 | 33.33% | live | +| seated | 1 | 33.33% | live | + +```python +def check_success(self): + return self.scene.success() +``` + +**Official dataset:** [EmbodiedSWE/pc_ram_franka_osc.jointpos_15hz_20260907](https://huggingface.co/datasets/EmbodiedSWE/pc_ram_franka_osc.jointpos_15hz_20260907) · 15 FPS · cameras: front, wrist + +**source task_index=0 / episode_index=0** · 2250 frames / 15 FPS / 150.00 s + +**Dataset instruction:** grip each stick by its top edge, lift it straight out of its holder, carry it over the case wall, line its gold edge connector up with its slot — the outermost and the second-from-socket, the alternating pair a dual-channel kit fills (the notch only fits one way — heat-spreader faces along the slot) — and press it straight down until it clicks fully home. A seated stick stays put on its own. The task is complete once both sticks are fully seated. A stick holds in a firm pinch: close the fingers flat across its faces near the top edge and the grip locks; open wide to release. + +![Source camera composite](media/pc_ram/episode_000000/preview.jpg) + +[同步 MP4](media/pc_ram/episode_000000/synchronized.mp4) · [front MP4](media/pc_ram/episode_000000/front.mp4) · [wrist MP4](media/pc_ram/episode_000000/wrist.mp4) · [官方轨迹 Parquet](trajectories/pc_ram/episode_000000.parquet) + +**source task_index=0 / episode_index=300** · 1290 frames / 15 FPS / 86.00 s + +**Dataset instruction:** grip each stick by its top edge, lift it straight out of its holder, carry it over the case wall, line its gold edge connector up with its slot — the outermost and the second-from-socket, the alternating pair a dual-channel kit fills (the notch only fits one way — heat-spreader faces along the slot) — and press it straight down until it clicks fully home. A seated stick stays put on its own. The task is complete once both sticks are fully seated. A stick holds in a firm pinch: close the fingers flat across its faces near the top edge and the grip locks; open wide to release. + +![Source camera composite](media/pc_ram/episode_000300/preview.jpg) + +[同步 MP4](media/pc_ram/episode_000300/synchronized.mp4) · [front MP4](media/pc_ram/episode_000300/front.mp4) · [wrist MP4](media/pc_ram/episode_000300/wrist.mp4) · [官方轨迹 Parquet](trajectories/pc_ram/episode_000300.parquet) + +**source task_index=0 / episode_index=599** · 600 frames / 15 FPS / 40.00 s + +**Dataset instruction:** grip each stick by its top edge, lift it straight out of its holder, carry it over the case wall, line its gold edge connector up with its slot — the outermost and the second-from-socket, the alternating pair a dual-channel kit fills (the notch only fits one way — heat-spreader faces along the slot) — and press it straight down until it clicks fully home. A seated stick stays put on its own. The task is complete once both sticks are fully seated. A stick holds in a firm pinch: close the fingers flat across its faces near the top edge and the grip locks; open wide to release. + +![Source camera composite](media/pc_ram/episode_000599/preview.jpg) + +[同步 MP4](media/pc_ram/episode_000599/synchronized.mp4) · [front MP4](media/pc_ram/episode_000599/front.mp4) · [wrist MP4](media/pc_ram/episode_000599/wrist.mp4) · [官方轨迹 Parquet](trajectories/pc_ram/episode_000599.parquet) + +[官网展示 MP4](media/pc_ram/website_demo.mp4) · 网站原始播放倍率 1.0x；该示例不等同于公开三相机训练轨迹。 + +## C10 · Pen holder · packing.pen_holder + +**Instruction / objective:** Insert every present pen tip-up and then place the filled holder upright. + +**Difficulty:** Easy · **官网列出的 evaluated on:** Franka / xArm7 / Gen3 + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=pen_holder) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/packing/grader/pen_holder.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| pens_in | 0.9 | 90.00% | live | +| holder_placed | 0.1 | 10.00% | live | + +```python +def check_success(self): + return self.scene.success() +``` + +**Official dataset:** [EmbodiedSWE/pen_holder_franka_diffik.jointtarget_15hz_20260909](https://huggingface.co/datasets/EmbodiedSWE/pen_holder_franka_diffik.jointtarget_15hz_20260909) · 15 FPS · cameras: front, corner, wrist + +**source task_index=0 / episode_index=0** · 3780 frames / 15 FPS / 252.00 s + +**Dataset instruction:** put every pencil into the holder tip-up (writing tip pointing out of the cup), then leave the loaded holder standing upright on the surface. You may hold the holder while filling it; a pencil dropped tip-down or left leaning outside the cup does not count, and a tipped-over holder scores nothing until it is stood up. + +![Source camera composite](media/pen_holder/episode_000000/preview.jpg) + +[同步 MP4](media/pen_holder/episode_000000/synchronized.mp4) · [front MP4](media/pen_holder/episode_000000/front.mp4) · [corner MP4](media/pen_holder/episode_000000/corner.mp4) · [wrist MP4](media/pen_holder/episode_000000/wrist.mp4) · [官方轨迹 Parquet](trajectories/pen_holder/episode_000000.parquet) + +**source task_index=0 / episode_index=342** · 900 frames / 15 FPS / 60.00 s + +**Dataset instruction:** put every pencil into the holder tip-up (writing tip pointing out of the cup), then leave the loaded holder standing upright on the surface. You may hold the holder while filling it; a pencil dropped tip-down or left leaning outside the cup does not count, and a tipped-over holder scores nothing until it is stood up. + +![Source camera composite](media/pen_holder/episode_000342/preview.jpg) + +[同步 MP4](media/pen_holder/episode_000342/synchronized.mp4) · [front MP4](media/pen_holder/episode_000342/front.mp4) · [corner MP4](media/pen_holder/episode_000342/corner.mp4) · [wrist MP4](media/pen_holder/episode_000342/wrist.mp4) · [官方轨迹 Parquet](trajectories/pen_holder/episode_000342.parquet) + +**source task_index=0 / episode_index=683** · 420 frames / 15 FPS / 28.00 s + +**Dataset instruction:** put every pencil into the holder tip-up (writing tip pointing out of the cup), then leave the loaded holder standing upright on the surface. You may hold the holder while filling it; a pencil dropped tip-down or left leaning outside the cup does not count, and a tipped-over holder scores nothing until it is stood up. + +![Source camera composite](media/pen_holder/episode_000683/preview.jpg) + +[同步 MP4](media/pen_holder/episode_000683/synchronized.mp4) · [front MP4](media/pen_holder/episode_000683/front.mp4) · [corner MP4](media/pen_holder/episode_000683/corner.mp4) · [wrist MP4](media/pen_holder/episode_000683/wrist.mp4) · [官方轨迹 Parquet](trajectories/pen_holder/episode_000683.parquet) + +[官网展示 MP4](media/pen_holder/website_demo.mp4) · 网站原始播放倍率 1.0x；该示例不等同于公开三相机训练轨迹。 + +## C11 · Tool packing · packing.tool_packing + +**Instruction / objective:** Stow three tools in their assigned drawers and close the cabinet. + +**Difficulty:** Medium · **官网列出的 evaluated on:** Franka / xArm7 / Gen3 + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=tool_packing) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/packing/grader/tool_packing.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| stowed | 0.45 | 45.00% | live | +| drawer_shut | 0.45 | 45.00% | live | +| cabinet_shut | 0.1 | 10.00% | live | + +```python +def check_success(self): + return self.scene.success() +``` + +**Official dataset:** [EmbodiedSWE/tool_packing_franka_diffik.jointtarget_15hz_20260910](https://huggingface.co/datasets/EmbodiedSWE/tool_packing_franka_diffik.jointtarget_15hz_20260910) · 15 FPS · cameras: front, corner, wrist + +**source task_index=0 / episode_index=0** · 8730 frames / 15 FPS / 582.00 s + +**Dataset instruction:** pull drawers open as needed and pack the tools — the STAPLER goes in the TOP (1st) drawer, the SCISSORS in the SECOND drawer, the KNIFE in the THIRD drawer (the 4th/bottom drawer stays shut and gets nothing) — then push every drawer shut. An item counts only inside its own drawer's tray; the job is done when all three tools are stowed and every drawer is shut. + +![Source camera composite](media/tool_packing/episode_000000/preview.jpg) + +[同步 MP4](media/tool_packing/episode_000000/synchronized.mp4) · [front MP4](media/tool_packing/episode_000000/front.mp4) · [corner MP4](media/tool_packing/episode_000000/corner.mp4) · [wrist MP4](media/tool_packing/episode_000000/wrist.mp4) · [官方轨迹 Parquet](trajectories/tool_packing/episode_000000.parquet) + +**source task_index=0 / episode_index=843** · 3720 frames / 15 FPS / 248.00 s + +**Dataset instruction:** pull drawers open as needed and pack the tools — the STAPLER goes in the TOP (1st) drawer, the SCISSORS in the SECOND drawer, the KNIFE in the THIRD drawer (the 4th/bottom drawer stays shut and gets nothing) — then push every drawer shut. An item counts only inside its own drawer's tray; the job is done when all three tools are stowed and every drawer is shut. + +![Source camera composite](media/tool_packing/episode_000843/preview.jpg) + +[同步 MP4](media/tool_packing/episode_000843/synchronized.mp4) · [front MP4](media/tool_packing/episode_000843/front.mp4) · [corner MP4](media/tool_packing/episode_000843/corner.mp4) · [wrist MP4](media/tool_packing/episode_000843/wrist.mp4) · [官方轨迹 Parquet](trajectories/tool_packing/episode_000843.parquet) + +**source task_index=0 / episode_index=1685** · 3600 frames / 15 FPS / 240.00 s + +**Dataset instruction:** pull drawers open as needed and pack the tools — the STAPLER goes in the TOP (1st) drawer, the SCISSORS in the SECOND drawer, the KNIFE in the THIRD drawer (the 4th/bottom drawer stays shut and gets nothing) — then push every drawer shut. An item counts only inside its own drawer's tray; the job is done when all three tools are stowed and every drawer is shut. + +![Source camera composite](media/tool_packing/episode_001685/preview.jpg) + +[同步 MP4](media/tool_packing/episode_001685/synchronized.mp4) · [front MP4](media/tool_packing/episode_001685/front.mp4) · [corner MP4](media/tool_packing/episode_001685/corner.mp4) · [wrist MP4](media/tool_packing/episode_001685/wrist.mp4) · [官方轨迹 Parquet](trajectories/tool_packing/episode_001685.parquet) + +[官网展示 MP4](media/tool_packing/website_demo.mp4) · 网站原始播放倍率 5.0x；该示例不等同于公开三相机训练轨迹。 + +## C12 · Egg carton · packing.egg_carton + +**Instruction / objective:** Seat three eggs upright in the cells of an egg carton, then push the hinged lid closed. + +**Difficulty:** Hard · **官网列出的 evaluated on:** Unitree G1 + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=egg_carton) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/packing/grader/egg_carton.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| eggs_seated | 0.9 | 90.00% | live | +| lid_closed | 0.1 | 10.00% | live | + +```python +def check_success(self): + return self.scene.success() +``` + +[官网展示 MP4](media/egg_carton/website_demo.mp4) · 网站原始播放倍率 1.0x；该示例不等同于公开三相机训练轨迹。 + +## C13 · Clear organics · packing.clear_organic_objects + +**Instruction / objective:** Clear the organic objects on a cluttered table into a bin while leaving the other items in place. + +**Difficulty:** Medium · **官网列出的 evaluated on:** Unitree G1 + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=clear_organic_objects) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/packing/grader/clear_organic_objects.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| organics_cleared | 1.0 | 100.00% | live | + +```python +def check_success(self): + return self.scene.success() & self._bin_upright() +``` + +**Official dataset:** [EmbodiedSWE/clear_organic_agent_ab_A400.jointtarget_20hz_20260918](https://huggingface.co/datasets/EmbodiedSWE/clear_organic_agent_ab_A400.jointtarget_20hz_20260918) · 20 FPS · cameras: front, side, wrist + +**source task_index=0 / episode_index=0** · 4797 frames / 20 FPS / 239.85 s + +**Dataset instruction:** identify every ORGANIC item — the fruits and vegetables — and place each one into the bin, leaving all the non-food clutter where it is. An item counts only when it is resting inside the bin; the job is done when every fruit and vegetable is in the bin and no non-food item has been put in. + +![Source camera composite](media/clear_organic_objects/episode_000000/preview.jpg) + +[同步 MP4](media/clear_organic_objects/episode_000000/synchronized.mp4) · [front MP4](media/clear_organic_objects/episode_000000/front.mp4) · [side MP4](media/clear_organic_objects/episode_000000/side.mp4) · [wrist MP4](media/clear_organic_objects/episode_000000/wrist.mp4) · [官方轨迹 Parquet](trajectories/clear_organic_objects/episode_000000.parquet) + +**source task_index=0 / episode_index=200** · 1879 frames / 20 FPS / 93.95 s + +**Dataset instruction:** identify every ORGANIC item — the fruits and vegetables — and place each one into the bin, leaving all the non-food clutter where it is. An item counts only when it is resting inside the bin; the job is done when every fruit and vegetable is in the bin and no non-food item has been put in. + +![Source camera composite](media/clear_organic_objects/episode_000200/preview.jpg) + +[同步 MP4](media/clear_organic_objects/episode_000200/synchronized.mp4) · [front MP4](media/clear_organic_objects/episode_000200/front.mp4) · [side MP4](media/clear_organic_objects/episode_000200/side.mp4) · [wrist MP4](media/clear_organic_objects/episode_000200/wrist.mp4) · [官方轨迹 Parquet](trajectories/clear_organic_objects/episode_000200.parquet) + +**source task_index=0 / episode_index=399** · 1320 frames / 20 FPS / 66.00 s + +**Dataset instruction:** identify every ORGANIC item — the fruits and vegetables — and place each one into the bin, leaving all the non-food clutter where it is. An item counts only when it is resting inside the bin; the job is done when every fruit and vegetable is in the bin and no non-food item has been put in. + +![Source camera composite](media/clear_organic_objects/episode_000399/preview.jpg) + +[同步 MP4](media/clear_organic_objects/episode_000399/synchronized.mp4) · [front MP4](media/clear_organic_objects/episode_000399/front.mp4) · [side MP4](media/clear_organic_objects/episode_000399/side.mp4) · [wrist MP4](media/clear_organic_objects/episode_000399/wrist.mp4) · [官方轨迹 Parquet](trajectories/clear_organic_objects/episode_000399.parquet) + +[官网展示 MP4](media/clear_organic_objects/website_demo.mp4) · 网站原始播放倍率 2.0x；该示例不等同于公开三相机训练轨迹。 + +## C14 · Coffee brewing · puzzle.coffee + +**Instruction / objective:** Load a capsule, brew coffee, and return the filled mug to the serving tray. + +**Difficulty:** Medium · **官网列出的 evaluated on:** Franka / xArm7 / Gen3 + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=coffee) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/puzzle/grader/coffee_service.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| pod_loaded | 1 | 14.29% | once | +| bay_closed | 1 | 14.29% | once | +| cup_staged | 1 | 14.29% | once | +| started | 1 | 14.29% | once | +| brewed | 1 | 14.29% | live | +| cup_out | 1 | 14.29% | once | +| served | 1 | 14.29% | live | + +```python +def check_success(self): + return self.scene.success() +``` + +**Official dataset:** [EmbodiedSWE/coffee_franka_joint.jointtarget_60hz_20260907](https://huggingface.co/datasets/EmbodiedSWE/coffee_franka_joint.jointtarget_60hz_20260907) · 60 FPS · cameras: front_wide, wrist + +**source task_index=0 / episode_index=0** · 8610 frames / 60 FPS / 143.50 s + +**Dataset instruction:** the mug filled by a completed brew and resting on the tray. The cover ridge, the capsule and the mug rim hold in a firm pinch: close the fingers across them and the grip locks; open wide to release. + +![Source camera composite](media/coffee/episode_000000/preview.jpg) + +[同步 MP4](media/coffee/episode_000000/synchronized.mp4) · [front_wide MP4](media/coffee/episode_000000/front_wide.mp4) · [wrist MP4](media/coffee/episode_000000/wrist.mp4) · [官方轨迹 Parquet](trajectories/coffee/episode_000000.parquet) + +**source task_index=0 / episode_index=300** · 8610 frames / 60 FPS / 143.50 s + +**Dataset instruction:** the mug filled by a completed brew and resting on the tray. The cover ridge, the capsule and the mug rim hold in a firm pinch: close the fingers across them and the grip locks; open wide to release. + +![Source camera composite](media/coffee/episode_000300/preview.jpg) + +[同步 MP4](media/coffee/episode_000300/synchronized.mp4) · [front_wide MP4](media/coffee/episode_000300/front_wide.mp4) · [wrist MP4](media/coffee/episode_000300/wrist.mp4) · [官方轨迹 Parquet](trajectories/coffee/episode_000300.parquet) + +**source task_index=0 / episode_index=599** · 1530 frames / 60 FPS / 25.50 s + +**Dataset instruction:** the mug filled by a completed brew and resting on the tray. The cover ridge, the capsule and the mug rim hold in a firm pinch: close the fingers across them and the grip locks; open wide to release. + +![Source camera composite](media/coffee/episode_000599/preview.jpg) + +[同步 MP4](media/coffee/episode_000599/synchronized.mp4) · [front_wide MP4](media/coffee/episode_000599/front_wide.mp4) · [wrist MP4](media/coffee/episode_000599/wrist.mp4) · [官方轨迹 Parquet](trajectories/coffee/episode_000599.parquet) + +[官网展示 MP4](media/coffee/website_demo.mp4) · 网站原始播放倍率 1.0x；该示例不等同于公开三相机训练轨迹。 + +## C15 · Spatula flip · puzzle.spatula + +**Instruction / objective:** Wedge a spatula under bread, flip it, reload it onto the blade, and serve it to a plate. + +**Difficulty:** Easy · **官网列出的 evaluated on:** Franka / xArm7 / Gen3 + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=spatula) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/puzzle/grader/spatula_flip_serve.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| lifted | 1 | 20.00% | once | +| wedged | 1 | 20.00% | once | +| flipped | 1 | 20.00% | once | +| loaded | 1 | 20.00% | once | +| served | 1 | 20.00% | live | + +```python +def check_success(self): + return self.scene.success() +``` + +[官网展示 MP4](media/spatula/website_demo.mp4) · 网站原始播放倍率 1.0x；该示例不等同于公开三相机训练轨迹。 + +## C16 · Syringe dosing · puzzle.syringe + +**Instruction / objective:** Draw liquid from a reservoir, meter three target doses, and repark the syringe. + +**Difficulty:** Hard · **官网列出的 evaluated on:** Bimanual Franka + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=syringe) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/puzzle/grader/syringe_dosing.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| drawn | 1 | 20.00% | once | +| dosed | 3 | 60.00% | live | +| parked | 1 | 20.00% | live | + +```python +def check_success(self): + return self.scene.success() +``` + +[官网展示 MP4](media/syringe/website_demo.mp4) · 网站原始播放倍率 1.0x；该示例不等同于公开三相机训练轨迹。 + +## C17 · Push shapes · puzzle.push_shapes + +**Instruction / objective:** Push three blocks onto their matching pads, correcting each block’s orientation along the way. + +**Difficulty:** Easy · **官网列出的 evaluated on:** Unitree G1 + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=push_shapes) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/puzzle/grader/push_shapes.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| yaw_aligned | 1 | 33.33% | live | +| near_pad | 1 | 33.33% | live | +| seated | 1 | 33.33% | live | + +```python +def check_success(self): + return self.scene.success() +``` + +[官网展示 MP4](media/push_shapes/website_demo.mp4) · 网站原始播放倍率 1.0x；该示例不等同于公开三相机训练轨迹。 + +## C18 · Classify objects · puzzle.classify_objects + +**Instruction / objective:** Sort scattered coloured blocks into the zones matching their colours. + +**Difficulty:** Easy · **官网列出的 evaluated on:** Unitree G1 + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=classify_objects) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/puzzle/grader/classify_objects.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| sorted_blocks | 1 | 100.00% | live | + +```python +def check_success(self): + return self.scene.success() +``` + +[官网展示 MP4](media/classify_objects/website_demo.mp4) · 网站原始播放倍率 1.0x；该示例不等同于公开三相机训练轨迹。 + +## C19 · Stack blocks · puzzle.stack_blocks + +**Instruction / objective:** Stack scattered blocks into a single aligned tower on a marked pad. + +**Difficulty:** Medium · **官网列出的 evaluated on:** Unitree G1 + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=stack_blocks) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/puzzle/grader/stack_blocks.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| tower | 1 | 100.00% | live | + +```python +def check_success(self): + return self.scene.success() +``` + +[官网展示 MP4](media/stack_blocks/website_demo.mp4) · 网站原始播放倍率 1.0x；该示例不等同于公开三相机训练轨迹。 + +## C20 · T-shirt folding · deformable.tshirt + +**Instruction / objective:** Fold a T-shirt into a compact, flat bundle. + +**Difficulty:** Medium · **官网列出的 evaluated on:** Franka / xArm7 / Gen3 + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=tshirt) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/deformable/grader/tshirt.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| sleeves | 2 | 40.00% | live | +| hem | 1 | 20.00% | live | +| folded | 1 | 20.00% | live | +| plausible | 1 | 20.00% | final | + +```python +def check_success(self): + return self.scene.success() +``` + +[官网展示 MP4](media/tshirt/website_demo.mp4) · 网站原始播放倍率 2.0x；该示例不等同于公开三相机训练轨迹。 + +## C21 · Latte pouring · deformable.latte + +**Instruction / objective:** Coordinate two arms to pour milk into a carried coffee mug without excessive spilling. + +**Difficulty:** Hard · **官网列出的 evaluated on:** Bimanual Franka + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=latte) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/deformable/grader/latte.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| pitcher_grasped | 1 | 25.00% | once | +| transferred | 1 | 25.00% | live | +| poured | 1 | 25.00% | live | +| plausible | 1 | 25.00% | final | + +```python +def check_success(self): + return self.scene.success() +``` + +[官网展示 MP4](media/latte/website_demo.mp4) · 网站原始播放倍率 3.0x；该示例不等同于公开三相机训练轨迹。 + +## C22 · Dough rolling · deformable.dumpling + +**Instruction / objective:** Roll a ball of dough flat into a round dumpling wrapper with a rolling pin. + +**Difficulty:** Hard · **官网列出的 evaluated on:** Franka / xArm7 / Gen3 + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=dumpling) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/deformable/grader/dumpling.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| flattened | 1 | 25.00% | live | +| spread | 1 | 25.00% | live | +| rolled | 1 | 25.00% | live | +| plausible | 1 | 25.00% | final | + +```python +def check_success(self): + return self.scene.success() +``` + +[官网展示 MP4](media/dumpling/website_demo.mp4) · 网站原始播放倍率 1.0x；该示例不等同于公开三相机训练轨迹。 + +## C23 · Shoelace half-knot · deformable.shoe_knot + +**Instruction / objective:** Tie a half knot from the two shoelaces of a sneaker. + +**Difficulty:** Hard · **官网列出的 evaluated on:** Franka / xArm7 / Gen3 + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=shoe_knot) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/deformable/grader/shoe_knot.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| wound | 1 | 25.00% | live | +| snug | 1 | 25.00% | live | +| tied | 1 | 25.00% | live | +| plausible | 1 | 25.00% | final | + +```python +def check_success(self): + import torch + + return torch.tensor([bool(self.scene.success(w)) for w in self._worlds()]) +``` + +官方目录中该任务目前没有公开展示 MP4。 + +## C24 · Banana slicing · cutting.slice + +**Instruction / objective:** Pick up a chef knife from its rest and slice a carrot (or a banana) into a target number of pieces on a chopping board. + +**Difficulty:** Easy · **官网列出的 evaluated on:** Franka / xArm7 / Gen3 + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=slice) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/cutting/grader/slice_food.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| knife_taken | 1 | 11.11% | once | +| planes_cut | 7 | 77.78% | live | +| sliced | 1 | 11.11% | live | + +```python +def check_success(self): + return self._sliced() +``` + +**Official dataset:** [EmbodiedSWE/cutting_slice_franka_diffik.jointtarget_15hz_train_20260919](https://huggingface.co/datasets/EmbodiedSWE/cutting_slice_franka_diffik.jointtarget_15hz_train_20260919) · 15 FPS · cameras: front, wrist + +**source task_index=0 / episode_index=0** · 1440 frames / 15 FPS / 96.00 s + +**Dataset instruction:** pick up the knife and press its edge down through each scored plane (blade aligned with the plane) to cut the banana into slices. The banana is cut once every plane has been pressed through and the pieces separate, all of them staying on the board. + +![Source camera composite](media/slice/episode_000000/preview.jpg) + +[同步 MP4](media/slice/episode_000000/synchronized.mp4) · [front MP4](media/slice/episode_000000/front.mp4) · [wrist MP4](media/slice/episode_000000/wrist.mp4) · [官方轨迹 Parquet](trajectories/slice/episode_000000.parquet) + +**source task_index=5 / episode_index=1055** · 1200 frames / 15 FPS / 80.00 s + +**Dataset instruction:** pick up the knife and press its edge down through each scored plane (blade aligned with the plane) to cut the carrot into slices. The carrot is cut once every plane has been pressed through and the pieces separate, all of them staying on the board. + +![Source camera composite](media/slice/episode_001055/preview.jpg) + +[同步 MP4](media/slice/episode_001055/synchronized.mp4) · [front MP4](media/slice/episode_001055/front.mp4) · [wrist MP4](media/slice/episode_001055/wrist.mp4) · [官方轨迹 Parquet](trajectories/slice/episode_001055.parquet) + +**source task_index=21 / episode_index=3664** · 1320 frames / 15 FPS / 88.00 s + +**Dataset instruction:** pick up the knife and press its edge down through each scored plane (blade aligned with the plane) to cut the baguette_1 into slices. The baguette_1 is cut once every plane has been pressed through and the pieces separate, all of them staying on the board. + +![Source camera composite](media/slice/episode_003664/preview.jpg) + +[同步 MP4](media/slice/episode_003664/synchronized.mp4) · [front MP4](media/slice/episode_003664/front.mp4) · [wrist MP4](media/slice/episode_003664/wrist.mp4) · [官方轨迹 Parquet](trajectories/slice/episode_003664.parquet) + +[官网展示 MP4](media/slice/website_demo.mp4) · 网站原始播放倍率 1.0x；该示例不等同于公开三相机训练轨迹。 + +## C25 · Food dicing · cutting.dice + +**Instruction / objective:** Dice a tomato (or a potato) into a 3×3 grid of pieces, cutting along two plane families with the knife re-yawed between them. + +**Difficulty:** Medium · **官网列出的 evaluated on:** Franka / xArm7 / Gen3 + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=dice) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/cutting/grader/dice_food.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| knife_taken | 1 | 7.14% | once | +| pairs_cut | 12 | 85.71% | live | +| diced | 1 | 7.14% | live | + +```python +def check_success(self): + return self._diced() +``` + +官方目录中该任务目前没有公开展示 MP4。 + +## C26 · Fruit delivery · locomanip.fruit_delivery + +**Instruction / objective:** Pick up every fruit on a long table and carry it around the table to a plate beyond arm reach. + +**Difficulty:** Medium · **官网列出的 evaluated on:** Unitree G1 + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=fruit_delivery) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/locomanip/grader/fruit_delivery.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| lifted | 1 | 33.33% | once | +| carried | 1 | 33.33% | once | +| placed | 1 | 33.33% | live | + +```python +def check_success(self): + return self.scene.success() +``` + +[官网展示 MP4](media/fruit_delivery/website_demo.mp4) · 网站原始播放倍率 1.0x；该示例不等同于公开三相机训练轨迹。 + +## C27 · Box to bin · locomanip.box_to_bin + +**Instruction / objective:** Pick a cardboard box off a shelf and carry it to a sorting bin on a table across the room. + +**Difficulty:** Medium · **官网列出的 evaluated on:** Unitree G1 + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=box_to_bin) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/locomanip/grader/box_to_bin.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| picked | 1 | 33.33% | once | +| carried | 1 | 33.33% | once | +| in_bin | 1 | 33.33% | live | + +```python +def check_success(self): + return self.scene.success() +``` + +[官网展示 MP4](media/box_to_bin/website_demo.mp4) · 网站原始播放倍率 1.0x；该示例不等同于公开三相机训练轨迹。 + +## C28 · Wheel carry · locomanip.wheel_carry + +**Instruction / objective:** Pick a steering wheel off one packing table and carry it to a basket on another, three metres away. + +**Difficulty:** Medium · **官网列出的 evaluated on:** Unitree G1 + +[MP4 浏览](https://travor278-embodiedswe-task-examples.static.hf.space/?task=wheel_carry) · [官方评分源码](https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/locomanip/grader/wheel_carry.py) + +| Stage | Weight | Normalized share | Mode | +|---|---|---|---| +| picked | 1 | 33.33% | once | +| carried | 1 | 33.33% | once | +| in_basket | 1 | 33.33% | live | + +```python +def check_success(self): + return self.scene.success() +``` + +[官网展示 MP4](media/wheel_carry/website_demo.mp4) · 网站原始播放倍率 1.0x；该示例不等同于公开三相机训练轨迹。 + +## 来源与复现 + +代码 revision：`433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b`。官网抓取日期：2026-10-05。 + +保留各数据源固定 revision、源文件大小与校验和、官方 episode 时间区间和元数据、源码评分说明。合并源视频采用 HTTP Range 读取选定 episode；不下载完整 assets、权重或整库。 + +脚本 `scripts/build_examples.py` 可基于保留的 manifests 运行 demos/extract/render/package；浏览页源码保留在 browser/ 与 scripts/build_browser.py。 + +官方代码采用 Apache-2.0；部分源数据未单列许可元数据。本整理保留各来源权利与署名，不另行扩张许可；见 NOTICE.md、LICENSE 和 reference/。 diff --git a/browser/base.html b/browser/base.html new file mode 100644 index 0000000000000000000000000000000000000000..f7ee44431d1833a348b389678eeb0bca4bec78d8 --- /dev/null +++ b/browser/base.html @@ -0,0 +1,116 @@ + + + + + +RoboDojo · Task browser + + + +
+ +
+
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"assembly.nut_thread.franka.joint", + "splits": { + "train": "0:401" + } +} \ No newline at end of file diff --git a/provenance/source_metadata/nut_thread/modality.json b/provenance/source_metadata/nut_thread/modality.json new file mode 100644 index 0000000000000000000000000000000000000000..43bb5a595a961fc88efd18990acabdff5d6b0341 --- /dev/null +++ b/provenance/source_metadata/nut_thread/modality.json @@ -0,0 +1,36 @@ +{ + "state": { + "arm_qpos": { + "start": 0, + "end": 7 + }, + "gripper_qpos": { + "start": 7, + "end": 8 + } + }, + "action": { + "arm_qpos_target": { + "start": 0, + "end": 7 + }, + "gripper_target": { + "start": 7, + "end": 8 + } + }, + "video": { + "front": { + "original_key": "observation.images.front" + }, + "side": { + "original_key": "observation.images.side" + }, + "wrist": { + "original_key": "observation.images.wrist" + } + }, + "annotation": { + "annotation.human.task_description": {} + } +} diff --git a/provenance/source_metadata/nut_thread/tasks.parquet b/provenance/source_metadata/nut_thread/tasks.parquet new file mode 100644 index 0000000000000000000000000000000000000000..f624cdbd7265ab13ee455a7793e5d4a1a364783c --- /dev/null +++ b/provenance/source_metadata/nut_thread/tasks.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6992465a735c7b853c3a9f5d4ef5a8c15fcae8cbd90cc23b1229fe1ebcfcc4ec +size 2904 diff --git a/provenance/source_metadata/pc_gpu/bake.json b/provenance/source_metadata/pc_gpu/bake.json new file mode 100644 index 0000000000000000000000000000000000000000..26b4e86d4addbb5c49a801e0889ea1be3c7f028a --- /dev/null +++ b/provenance/source_metadata/pc_gpu/bake.json @@ -0,0 +1,783 @@ +{ + "control_space": "joint_pos", + "control_freq_hz": 15.0, + "origin": [ + "sim" + ], + "robot_type": "assembly.pc_gpu.franka.osc", + "state_names": [ + "panda_joint1", + "panda_joint2", + "panda_joint3", + "panda_joint4", + "panda_joint5", + "panda_joint6", + "panda_joint7", + "gripper" + ], + "action_names": [ + 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"panda_joint2", + "panda_joint3", + "panda_joint4", + "panda_joint5", + "panda_joint6", + "panda_joint7", + "gripper" + ] + }, + "action": { + "dtype": "float32", + "shape": [ + 8 + ], + "names": [ + "panda_joint1", + "panda_joint2", + "panda_joint3", + "panda_joint4", + "panda_joint5", + "panda_joint6", + "panda_joint7", + "gripper" + ] + }, + "raw_command": { + "dtype": "float32", + "shape": [ + 8 + ], + "names": [ + "cmd_0", + "cmd_1", + "cmd_2", + "cmd_3", + "cmd_4", + "cmd_5", + "cmd_6", + "cmd_7" + ] + }, + "timestamp": { + "dtype": "float32", + "shape": [ + 1 + ], + "names": null + }, + "frame_index": { + "dtype": "int64", + "shape": [ + 1 + ], + "names": null + }, + "episode_index": { + "dtype": "int64", + "shape": [ + 1 + ], + "names": null + }, + "index": { + "dtype": "int64", + "shape": [ + 1 + ], + "names": null + }, + "task_index": { + "dtype": "int64", + "shape": [ + 1 + ], + "names": null + } + } +} \ No newline at end of file diff --git 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b/provenance/source_metadata/pc_gpu/tasks.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:af22ac4eb81d9c485dce86d63059815d567869ee94794da6dd7aa80d6e13ff3e +size 6553 diff --git a/provenance/source_metadata/pc_gpu_ram/bake.json b/provenance/source_metadata/pc_gpu_ram/bake.json new file mode 100644 index 0000000000000000000000000000000000000000..9b14c27835399d49afdde3659fd706fd10c9d039 --- /dev/null +++ b/provenance/source_metadata/pc_gpu_ram/bake.json @@ -0,0 +1,783 @@ +{ + "control_space": "joint_pos", + "control_freq_hz": 15.0, + "origin": [ + "sim" + ], + "robot_type": "assembly.pc_gpu_ram.franka.osc", + "state_names": [ + "panda_joint1", + "panda_joint2", + "panda_joint3", + "panda_joint4", + "panda_joint5", + "panda_joint6", + "panda_joint7", + "gripper" + ], + "action_names": [ + "panda_joint1", + "panda_joint2", + "panda_joint3", + "panda_joint4", + "panda_joint5", + "panda_joint6", + "panda_joint7", + "gripper" + ], + "state_parts": { + 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b/provenance/source_metadata/pc_gpu_ram/info.json new file mode 100644 index 0000000000000000000000000000000000000000..d2db90c1a7c0b0086c7ff1849996ff98b76b0802 --- /dev/null +++ b/provenance/source_metadata/pc_gpu_ram/info.json @@ -0,0 +1,170 @@ +{ + "codebase_version": "v3.0", + "robot_type": "assembly.pc_gpu_ram.franka.osc", + "total_episodes": 600, + "total_frames": 1400130, + "total_tasks": 3, + "chunks_size": 1000, + "data_files_size_in_mb": 100, + "video_files_size_in_mb": 78, + "fps": 15, + "splits": { + "train": "0:600" + }, + "data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet", + "video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4", + "features": { + "observation.images.front": { + "dtype": "video", + "shape": [ + 480, + 640, + 3 + ], + "names": [ + "height", + "width", + "channels" + ], + "info": { + "video.height": 480, + "video.width": 640, + "video.codec": "h264", + "video.pix_fmt": "yuv420p", + "video.is_depth_map": false, + "video.fps": 15, + "video.channels": 3, + "has_audio": false + } + }, + "observation.images.high": { + "dtype": "video", + "shape": [ + 480, + 640, + 3 + ], + "names": [ + "height", + "width", + "channels" + ], + "info": { + "video.height": 480, + "video.width": 640, + "video.codec": "h264", + "video.pix_fmt": "yuv420p", + "video.is_depth_map": false, + "video.fps": 15, + "video.channels": 3, + "has_audio": false + } + }, + "observation.images.wrist": { + "dtype": "video", + "shape": [ + 480, + 640, + 3 + ], + "names": [ + "height", + "width", + "channels" + ], + "info": { + "video.height": 480, + "video.width": 640, + "video.codec": "h264", + "video.pix_fmt": "yuv420p", + "video.is_depth_map": false, + "video.fps": 15, + "video.channels": 3, + "has_audio": false + } + }, + "observation.state": { + "dtype": "float32", + "shape": [ + 8 + ], + "names": [ + "panda_joint1", + "panda_joint2", + "panda_joint3", + "panda_joint4", + "panda_joint5", + "panda_joint6", + "panda_joint7", + "gripper" + ] + }, + "action": { + "dtype": "float32", + "shape": [ + 8 + ], + "names": [ + "panda_joint1", + "panda_joint2", + "panda_joint3", + "panda_joint4", + "panda_joint5", + "panda_joint6", + "panda_joint7", + "gripper" + ] + }, + "raw_command": { + "dtype": "float32", + "shape": [ + 8 + ], + "names": [ + "cmd_0", + "cmd_1", + "cmd_2", + "cmd_3", + "cmd_4", + "cmd_5", + "cmd_6", + "cmd_7" + ] + }, + "timestamp": { + "dtype": "float32", + "shape": [ + 1 + ], + "names": null + }, + "frame_index": { + "dtype": "int64", + "shape": [ + 1 + ], + "names": null + }, + "episode_index": { + "dtype": "int64", + "shape": [ + 1 + ], + "names": null + }, + "index": { + "dtype": "int64", + "shape": [ + 1 + ], + "names": null + }, + "task_index": { + "dtype": "int64", + "shape": [ + 1 + ], + "names": null + } + } +} \ No newline at end of file diff --git a/provenance/source_metadata/pc_gpu_ram/modality.json b/provenance/source_metadata/pc_gpu_ram/modality.json new file mode 100644 index 0000000000000000000000000000000000000000..b984b099145753b8fbe309a3562a47cbad6400ac --- /dev/null +++ b/provenance/source_metadata/pc_gpu_ram/modality.json @@ -0,0 +1,36 @@ +{ + "state": { + "arm_qpos": { + "start": 0, + "end": 7 + }, + "gripper_qpos": { + "start": 7, + "end": 8 + } + }, + "action": { + "arm_qpos": { + "start": 0, + "end": 7 + }, + "gripper_qpos": { + "start": 7, + "end": 8 + } + }, + "video": { + "front": { + "original_key": "observation.images.front" + }, + "high": { + "original_key": "observation.images.high" + }, + "wrist": { + "original_key": "observation.images.wrist" + } + }, + "annotation": { + "annotation.human.task_description": {} + } +} diff --git a/provenance/source_metadata/pc_gpu_ram/tasks.parquet b/provenance/source_metadata/pc_gpu_ram/tasks.parquet new file mode 100644 index 0000000000000000000000000000000000000000..a96463a1318c9ca0ed4d3ddeff65539402875363 --- /dev/null +++ b/provenance/source_metadata/pc_gpu_ram/tasks.parquet @@ -0,0 +1,3 @@ +version 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null + }, + "index": { + "dtype": "int64", + "shape": [ + 1 + ], + "names": null + }, + "task_index": { + "dtype": "int64", + "shape": [ + 1 + ], + "names": null + } + }, + "total_episodes": 1686, + "total_frames": 7051225, + "total_tasks": 1, + "chunks_size": 1000, + "data_files_size_in_mb": 100, + "video_files_size_in_mb": 46, + "data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet", + "video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4", + "robot_type": "packing.tool_packing.franka.diff_ik", + "splits": { + "train": "0:1686" + } +} \ No newline at end of file diff --git a/provenance/source_metadata/tool_packing/modality.json b/provenance/source_metadata/tool_packing/modality.json new file mode 100644 index 0000000000000000000000000000000000000000..67c6f2653fc5e5f0a765c0985f82427fec00450b --- /dev/null +++ b/provenance/source_metadata/tool_packing/modality.json @@ -0,0 +1,36 @@ +{ + "state": { + "arm_qpos": { + "start": 0, + "end": 7 + }, + "gripper_qpos": { + "start": 7, + "end": 8 + } + }, + "action": { + "arm_qpos_target": { + "start": 0, + "end": 7 + }, + "gripper_target": { + "start": 7, + "end": 8 + } + }, + "video": { + "corner": { + "original_key": "observation.images.corner" + }, + "front": { + "original_key": "observation.images.front" + }, + "wrist": { + "original_key": "observation.images.wrist" + } + }, + "annotation": { + "annotation.human.task_description": {} + } +} diff --git a/provenance/source_metadata/tool_packing/tasks.parquet b/provenance/source_metadata/tool_packing/tasks.parquet new file mode 100644 index 0000000000000000000000000000000000000000..4d00561006909f39b6f320efcfb16105f1e85209 --- /dev/null +++ b/provenance/source_metadata/tool_packing/tasks.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b3121e66081cee058075ec00e1451752592c58ffb312c5af550e313a4d7476b3 +size 3754 diff --git a/provenance/tasks.json b/provenance/tasks.json new file mode 100644 index 0000000000000000000000000000000000000000..272273996b2a30ec0e748f1dca67eea34e7e2ee4 --- /dev/null +++ b/provenance/tasks.json @@ -0,0 +1,1527 @@ +[ + { + "catalog_number": 1, + "scene_key": "allen_bolt", + "official_scene_id": "assembly.allen_bolt", + "category": "Assembly", + "name": "Allen bolt", + "instruction": "Drive an Allen bolt into a threaded plate using an L-shaped key.", + "difficulty": "Medium", + "catalog_embodiments": "Franka / xArm7 / Gen3", + "rubric": [ + { + "stage": "key_grasped", + "weight": 1, + "normalized_weight": 0.5, + "mode": "once" + }, + { + "stage": "driven", + "weight": 1, + "normalized_weight": 0.5, + "mode": "live" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the allen_bolt scene: every bolt driven home with the key.\n\nRubric stages (weights and modes live in RUBRIC):\n key_grasped a key has been taken in the weld-on-closure grip (`scene.grasp_held`, any\n key, either arm) — the scene's own grasp state; a milestone, latched. Also\n true once every bolt is seated (a seated bolt was necessarily driven by\n the key), so the rung nests under `driven`. Under an embodiment with no\n gripper (the weld contract disabled, e.g. robot=\"null\") the grasp state does\n not exist and the rung reads from that implication alone\n driven mean over bolts of the tip's depth from the start register to the seat —\n `bolt_stage_depth` -> `seat_depth` when the bolts spawn hand-started\n (`bolt_staged`, every robot binding), plate top (depth 0) -> `seat_depth`\n for the lying spawn — counted only while the bolt is within `align_xy` of the\n hole axis and `align_axis_deg` of upright, the scene's own non-depth seat\n gates, so a bolt's value is 1.0 iff the scene calls it seated\nBoth stages are per env. The key's own \"seated in the hex socket\" step is NOT a rung: the\nhead/socket geometry is not in the scene cfg, so it cannot be read from state without an\ninvented tolerance. Success is the scene's `success()` per env (every bolt `seated()`),\nread when the delivery finishes.", + "grader_summary": "Every bolt seated — driven down to seat depth with the key, upright on the hole axis.\n\nLadder: key_grasped 0.50 · driven 1.00 — two equal rungs for the two measurable steps\n(take the key, drive the bolt home); the second is a linear ramp of tip depth from the\nbolt's start register (`bolt_stage_depth`, or the plate top for a lying spawn) to\n`seat_depth`. With N bolts `driven` is the mean over bolts.", + "success_source": "def check_success(self):\n return self.scene.success()", + "grader_source_path": "robobench/suites/assembly/grader/allen_bolt_assembly.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/assembly/grader/allen_bolt_assembly.py", + "gallery": { + "path": "media/allen_bolt/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/allen_bolt.mp4", + "website_playback_rate": 1.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "842d7c59cfc60edd4db132d1483627096a03ed9376c93d1b59652bdebaeec134", + "bytes": 384758 + }, + "dataset_source": null + }, + { + "catalog_number": 2, + "scene_key": "bulb", + "official_scene_id": "assembly.bulb", + "category": "Assembly", + "name": "Bulb screwing", + "instruction": "Pick up a light bulb and screw it into its socket until fully seated.", + "difficulty": "Medium", + "catalog_embodiments": "Franka / xArm7 / Gen3", + "rubric": [ + { + "stage": "lifted", + "weight": 1, + "normalized_weight": 0.3333333333333333, + "mode": "once" + }, + { + "stage": "engaged", + "weight": 1, + "normalized_weight": 0.3333333333333333, + "mode": "live" + }, + { + "stage": "threaded", + "weight": 1, + "normalized_weight": 0.3333333333333333, + "mode": "live" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the bulb scene: every bulb seated.\n\nRubric stages (weights and modes live in RUBRIC), each a fraction over that env's bulbs:\n lifted bulbs ever raised to the socket's bore-mouth height — origin at/above\n `socket_opening_z` in the nearest socket's frame, the height a cap-down bulb\n has to clear before it can be set on the socket — or already in a bore (a bulb\n in the bore was necessarily lifted there); a milestone, latched per bulb\n engaged bulbs on/in a socket bore — within `align_xy` of the axis and at/below the\n bore mouth (`socket_opening_z`)\n threaded mean thread depth over the scene's own glow band, from `light_start_z` (free\n rest on the thread, where the glow begins) to `seat_z` (seated), counted only\n while the bulb is aligned in xy AND tilt exactly as the scene's `seated()`\n demands — so a bulb's value is 1.0 iff the scene calls it seated\nThe rungs nest (seated => engaged => lifted), so progress reaches 1.0 exactly when every\nbulb of the env is seated, and for a grader built after the pick as well as one that\nwatched it. Success is the scene's `seated()` per env, read when the delivery finishes.", + "grader_summary": "Every bulb seated — threaded to depth, on the socket axis, upright.\n\nLadder: lifted 0.33 · engaged 0.67 · threaded 1.00 — three equal rungs for the three\nsteps of the task (pick the bulb up, set it on the socket, screw it home); the last\nrung is a linear ramp of thread depth over the scene's own glow band\n(`light_start_z` -> `seat_z`). With N bulbs every stage is the fraction of bulbs.", + "success_source": "def check_success(self):\n return self.scene.seated().all(dim=1)", + "grader_source_path": "robobench/suites/assembly/grader/bulb_assembly.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/assembly/grader/bulb_assembly.py", + "gallery": { + "path": "media/bulb/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/bulb.mp4", + "website_playback_rate": 5.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "0643ff21afba057dff61af5e35411ddaf9552e794a730cad9ed953d83de4f6eb", + "bytes": 295291 + }, + "dataset_source": { + "repo_id": "EmbodiedSWE/bulb_franka_pinkik.jointtarget_15hz_generalization_train_20260921", + "revision": "53035d12cccc88f1d68a8fb75a12092e8739952e", + "format": "v3.0", + "fps": 15, + "total_episodes": 816, + "camera_order": [ + "front", + "wrist" + ], + "task_index_scope": "local to this source repository; not a benchmark-global task ID", + "robot_type": "assembly.bulb.franka.pink_ik", + "selected_episodes": [ + 0, + 408, + 815 + ], + "source_license_metadata": "None" + } + }, + { + "catalog_number": 3, + "scene_key": "ikea_table", + "official_scene_id": "assembly.ikea_table", + "category": "Assembly", + "name": "Table assembly", + "instruction": "Attach four threaded legs to the corner studs of a tabletop.", + "difficulty": "Hard", + "catalog_embodiments": "Bimanual Franka", + "rubric": [ + { + "stage": "aligned", + "weight": 1, + "normalized_weight": 0.5, + "mode": "live" + }, + { + "stage": "seated", + "weight": 1, + "normalized_weight": 0.5, + "mode": "live" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the ikea_table scene: all four legs screwed down onto the tabletop's studs.\n\nThe four legs are identical sub-goals, so every rung is ONE stage whose value is the fraction\nof legs (k/4) that have reached it. The scene has no grasp contract and exposes no threshold\nfor \"lifted\", so a grasped/lifted rung cannot be measured from state and is not one; per leg\nthe ladder is aligned -> seated, equal weights, all measured in the tabletop's own frame\n(order-independent: any leg on any stud, as the scene's `seated()` is):\n aligned legs standing on a stud — within `align_xy` of the nearest stud and screw axis\n within `align_axis_deg` of the stud axis: the scene's own two non-depth seat gates\n seated mean over legs of the screw-down from the stud-top rest height (`leg_start_z`, the\n scene's on-stud spawn height) to the seat (`seat_z`), counted only while aligned —\n so a leg's value is 1.0 iff the scene calls it `seated()`\nThe rungs nest (seated => aligned), so progress reaches 1.0 exactly when every leg is seated.\nThe scene welds a seated leg to the table, so a welded leg stays `seated()` however the\nassembly is later moved. Success is the scene's own `success()` (every leg `seated()`), read\nwhen the delivery finishes.", + "grader_summary": "All four legs seated on the studs — each threaded down to depth, on a stud axis, upright.\n\nLadder (per leg, each rung 1/8): aligned 1/8 · seated 2/8;\n1 leg 0.25 · 2 legs 0.50 · 3 legs 0.75 · 4 legs 1.00 — two equal rungs for the two\nmeasurable steps of each leg (set it on its stud, screw it down); the second is a linear\nramp of the leg's height from the stud-top rest height (`leg_start_z`) to the seat (`seat_z`).", + "success_source": "def check_success(self):\n return self.scene.success()", + "grader_source_path": "robobench/suites/assembly/grader/ikea_table_assembly.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/assembly/grader/ikea_table_assembly.py", + "gallery": { + "path": "media/ikea_table/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/ikea_table.mp4", + "website_playback_rate": 4.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "5f68332685fb6baacb949ab2bac84a89066ffee12682f63d1143613630a100c5", + "bytes": 1151335 + }, + "dataset_source": null + }, + { + "catalog_number": 4, + "scene_key": "so101", + "official_scene_id": "assembly.so101", + "category": "Assembly", + "name": "Robot-arm assembly", + "instruction": "Assemble a robot arm.", + "difficulty": "Hard", + "catalog_embodiments": "Bimanual Franka", + "rubric": [ + { + "stage": "motor_seated", + "weight": 1, + "normalized_weight": 0.07142857142857142, + "mode": "live" + }, + { + "stage": "tab_screws_picked", + "weight": 2, + "normalized_weight": 0.14285714285714285, + "mode": "once" + }, + { + "stage": "tab_screws_fastened", + "weight": 2, + "normalized_weight": 0.14285714285714285, + "mode": "live" + }, + { + "stage": "fork_seated", + "weight": 1, + "normalized_weight": 0.07142857142857142, + "mode": "live" + }, + { + "stage": "horn_screws_picked", + "weight": 4, + "normalized_weight": 0.2857142857142857, + "mode": "once" + }, + { + "stage": "horn_screws_fastened", + "weight": 4, + "normalized_weight": 0.2857142857142857, + "mode": "live" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the so101 scene: the elbow servo seated + tab-screwed into the upper arm, the forearm\nclipped onto its horn + horn-screwed.\n\nThe scene defines no `success()`; the criterion below is read off its own assembly geometry\nand fastening state (see `check_success`):\n every loose screw fastened — the four identical M2 tab screws in the four tab holes and the\n eight identical M3 horn screws in the eight horn-line holes (the scene's own `state == 2`,\n \"fully fastened\") — with the servo live in the arm pocket and the forearm live on the horn.\n\nRubric stages (weights and modes live in RUBRIC):\n motor_seated the bare servo sits in the upper arm's pocket: its body frame on the\n upper_arm link frame (identical by construction when seated) within the\n scene's fastening-gate tolerances `motor_align_pos` / `motor_align_deg`\n — the scene's own PARTS ALIGNED rule for the tab holes\n tab_screws_picked fraction of the four identical M2 tab screws ever carried on the drill's\n magnetic bit (`attached`, the scene's only \"grasp\" of a screw; a\n fastened screw was necessarily driven off that bit) — a milestone\n tab_screws_fastened fraction of the four M2s welded home in a tab hole (`fastened`)\n fork_seated the forearm's clevis sits on the servo's output horn: the lower_arm at\n the elbow-joint seat transform of the motor, within the same\n `motor_align_pos` / `motor_align_deg` gate, angle-agnostic about the\n horn axis (the assembled elbow may stand at any angle) — the scene's\n PARTS ALIGNED rule for the horn holes\n horn_screws_picked fraction of the eight identical M3 horn screws ever carried on the bit\n horn_screws_fastened fraction of the eight M3s welded home in a horn-line hole\nEvery stage is per env. The two \"picked\" milestones are the grasped rung of the screws' ladder;\nthe servo and the forearm have no grasp the scene can measure (no grasp contract, no lift\nthreshold in cfg), so their ladders start at the seated rung. A screw's transient \"in the hole,\nnot yet home\" state is not a rung: the auto-driver takes it from engaged to fastened by itself.", + "grader_summary": "The SO101 elbow assembled: servo seated and tab-screwed into the upper arm, forearm clipped onto the horn and horn-screwed.\n\nLadder (14 units — one unit per part: servo 1, 4 M2 tab screws 4, forearm 1, 8 M3 horn\nscrews 8; a screw's unit split equally over its two rungs, picked 0.5 + fastened 0.5):\n servo in pocket 1/14 = 0.071 · 4 M2 on the bit 3/14 = 0.214 · 4 M2 fastened 5/14 = 0.357 ·\n forearm on horn 6/14 = 0.429 · 8 M3 on the bit 10/14 = 0.714 · 8 M3 fastened 14/14 = 1.000\nThe identical screws of each group share one k/N fraction stage weighted N/2 per rung.", + "success_source": "def check_success(self):\n \"\"\"Every loose screw fastened (the scene's `state == 2`) AND the servo live in the\n pocket AND the forearm live on the horn — the assembled elbow, on the final state.\"\"\"\n all_fastened = (self.scene.fastened >= 0).all(dim=1)\n return all_fastened & self._motor_seated() & self._fork_seated()", + "grader_source_path": "robobench/suites/assembly/grader/so101_assembly.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/assembly/grader/so101_assembly.py", + "gallery": { + "path": "media/so101/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/so101.mp4", + "website_playback_rate": 1.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "e11560d36e25e18a217b14098189c598b870978a562bfe6d3c16dae268af3a0f", + "bytes": 928445 + }, + "dataset_source": null + }, + { + "catalog_number": 5, + "scene_key": "nut_thread", + "official_scene_id": "assembly.nut_thread", + "category": "Assembly", + "name": "Nut threading", + "instruction": "Pick up an M16 nut and thread it onto a fixed bolt.", + "difficulty": "Easy", + "catalog_embodiments": "Franka / xArm7 / Gen3", + "rubric": [ + { + "stage": "lifted", + "weight": 1, + "normalized_weight": 0.3333333333333333, + "mode": "once" + }, + { + "stage": "aligned", + "weight": 1, + "normalized_weight": 0.3333333333333333, + "mode": "live" + }, + { + "stage": "threaded", + "weight": 1, + "normalized_weight": 0.3333333333333333, + "mode": "live" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the nut_thread scene: every nut seated on a bolt.\n\nRubric stages (weights and modes live in RUBRIC), each a fraction over that env's nuts:\n lifted nuts ever raised to the bolt-top rest height — origin at/above `bolt_height`\n in the nearest bolt's frame, the height the scene quotes for a nut resting on\n the bolt top and so the height a nut has to clear before it can be set on the\n bolt — or already aligned on a bolt (a nut on the bolt was necessarily lifted\n there); a milestone, latched per nut\n aligned nuts aligned on a bolt — within `align_xy` of the bolt axis and tilted at most\n `align_axis_deg` off it, the scene's own two non-depth seat gates\n threaded mean thread depth from the bolt-top rest height (`bolt_height`) down to the\n seat (`seat_z`), counted only while aligned — so a nut's value is 1.0 iff the\n scene calls it seated\nThe rungs nest (seated => aligned => lifted), so progress reaches 1.0 exactly when every\nnut of the env is seated, and for a grader built after the pick as well as one that watched\nit. Success is the scene's `seated()` per env, read when the delivery finishes.", + "grader_summary": "Every nut seated — threaded down to seat depth, on the bolt axis, upright.\n\nLadder: lifted 0.33 · aligned 0.67 · threaded 1.00 — three equal rungs for the three\nsteps of the task (pick the nut up, set it on the bolt, screw it down); the last rung\nis a linear ramp of the nut's height from the bolt-top rest height (`bolt_height`) to\nthe seat (`seat_z`). With N nuts every stage is the fraction of nuts.", + "success_source": "def check_success(self):\n return self.scene.seated().all(dim=1)", + "grader_source_path": "robobench/suites/assembly/grader/nut_thread_assembly.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/assembly/grader/nut_thread_assembly.py", + "gallery": { + "path": "media/nut_thread/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/nut_thread.mp4", + "website_playback_rate": 1.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "23ea1300efdc4a4b63d272143b44b60c10c74a4bcd9e5e7ef190d22b020200d3", + "bytes": 178375 + }, + "dataset_source": { + "repo_id": "EmbodiedSWE/nut_thread_franka_ik.jointtarget_20hz_20260905", + "revision": "7c9282badf83850cca7c56159f2e048ad62cb93a", + "format": "v3.0", + "fps": 20, + "total_episodes": 401, + "camera_order": [ + "front", + "side", + "wrist" + ], + "task_index_scope": "local to this source repository; not a benchmark-global task ID", + "robot_type": "assembly.nut_thread.franka.joint", + "selected_episodes": [ + 0, + 200, + 400 + ], + "source_license_metadata": "license: apache-2.0\ntask_categories:\n- robotics\ntags:\n- LeRobot\n- robotics\n- franka\n- assembly\n- nut-threading\n- isaac-sim\n- cosigen\n- sim\nconfigs:\n- config_name: default\n data_files: data/*/*.parquet" + } + }, + { + "catalog_number": 6, + "scene_key": "pc_gpu", + "official_scene_id": "assembly.pc_gpu", + "category": "Assembly", + "name": "GPU insertion", + "instruction": "Align a graphics card with a PCIe slot and press it into place.", + "difficulty": "Easy", + "catalog_embodiments": "Franka / xArm7 / Gen3", + "rubric": [ + { + "stage": "grasped", + "weight": 1, + "normalized_weight": 0.3333333333333333, + "mode": "once" + }, + { + "stage": "aligned", + "weight": 1, + "normalized_weight": 0.3333333333333333, + "mode": "live" + }, + { + "stage": "pressed", + "weight": 1, + "normalized_weight": 0.3333333333333333, + "mode": "live" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the pc_gpu scene: the graphics card seated in the PCIe x16 slot.\n\nRubric stages (weights and modes live in RUBRIC):\n grasped the card has been taken in the weld-on-closure grip (`scene.grasp_held`) — the\n scene's own grasp state; a milestone, latched. Also true once the card is\n aligned in the slot (a card in the slot was necessarily carried there), so the\n rung nests under `aligned`. Under an embodiment with no gripper (the weld\n contract disabled, e.g. robot=\"null\") the grasp state does not exist and the\n rung reads from that implication alone\n aligned the card lined up on the slot — origin within `align_xy` of the seated point,\n up axis within `align_axis_deg` of the slot axis, length axis within\n `align_yaw_deg` of the slot heading: the scene's own three non-depth seat gates\n pressed tab depth below the slot mouth (`slot_mouth_z`, the scene's own `engaged()`)\n as a fraction of `seat_depth`, counted only while aligned — so it is 1.0 iff\n the scene calls the card seated\nEvery stage is per env. The rungs nest (seated => aligned => grasped), so progress reaches\n1.0 exactly when the card is seated, and for a grader built after the pick as well as one\nthat watched it. The rear-panel pass-through is NOT a rung: the cutout's geometry is not in\nthe scene cfg, so it cannot be read from state without an invented tolerance. Success is\nthe scene's `seated()` per env — depth, xy, tilt AND heading gates — read when the\ndelivery finishes.", + "grader_summary": "The card seated in its slot — pressed to depth, centred, upright, heading along the slot.\n\nLadder: grasped 0.33 · aligned 0.67 · pressed 1.00 — three equal rungs for the three\nsteps of the task (take the card, line it up on the slot, press it home); the last rung\nis a linear ramp of tab depth from the slot mouth (`slot_mouth_z`) to `seat_depth`.", + "success_source": "def check_success(self):\n return self.scene.seated()", + "grader_source_path": "robobench/suites/assembly/grader/pc_gpu_assembly.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/assembly/grader/pc_gpu_assembly.py", + "gallery": { + "path": "media/pc_gpu/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/pc_gpu.mp4", + "website_playback_rate": 1.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "ccb137b362404bc9991eca23a8b1aa857b66bec80a3f5240490ef3f05f95878c", + "bytes": 225359 + }, + "dataset_source": { + "repo_id": "EmbodiedSWE/pc_gpu_franka_osc.jointpos_15hz_20260907", + "revision": "d9d2cb6eb8288a2716758bb3790fe0d37798990b", + "format": "v3.0", + "fps": 15, + "total_episodes": 600, + "camera_order": [ + "front", + "wrist" + ], + "task_index_scope": "local to this source repository; not a benchmark-global task ID", + "robot_type": "assembly.pc_gpu.franka.osc", + "selected_episodes": [ + 0, + 49, + 114 + ], + "source_license_metadata": "license: apache-2.0\ntask_categories:\n- robotics\ntags:\n- LeRobot\n- franka\n- assembly\n- pc-gpu\n- isaac-sim\n- cosigen\n- sim\n- data-gen\nconfigs:\n- config_name: default\n data_files: data/*/*.parquet" + } + }, + { + "catalog_number": 7, + "scene_key": "pc_gpu_ram", + "official_scene_id": "assembly.pc_gpu_ram", + "category": "Assembly", + "name": "GPU + RAM install", + "instruction": "Install a graphics card and then seat two memory modules in their slots.", + "difficulty": "Medium", + "catalog_embodiments": "Franka / xArm7 / Gen3", + "rubric": [ + { + "stage": "gpu_grasped", + "weight": 1, + "normalized_weight": 0.1111111111111111, + "mode": "once" + }, + { + "stage": "gpu_aligned", + "weight": 1, + "normalized_weight": 0.1111111111111111, + "mode": "live" + }, + { + "stage": "gpu_seated", + "weight": 1, + "normalized_weight": 0.1111111111111111, + "mode": "live" + }, + { + "stage": "ram_grasped", + "weight": 2, + "normalized_weight": 0.2222222222222222, + "mode": "once" + }, + { + "stage": "ram_aligned", + "weight": 2, + "normalized_weight": 0.2222222222222222, + "mode": "live" + }, + { + "stage": "ram_seated", + "weight": 2, + "normalized_weight": 0.2222222222222222, + "mode": "live" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the pc_gpu_ram scene: the graphics card AND both RAM sticks seated.\n\nThree parts — one card, two identical sticks. Each part climbs the same ladder, grasped ->\naligned -> seated, and every part is worth the same third of the score. The card has its own\nthree stages; the two identical sticks share three FRACTION stages (k/2 each), which\ntherefore carry weight 2 (one unit per stick) against the card's 1.\n gpu_grasped / ram_grasped parts ever taken in the weld-on-closure grip (`scene.grasp_held`:\n column 0 the card, 1.. the sticks, in `grasp_sites()` order) —\n the scene's own grasp state; milestones, latched per part. Also\n true for a part that is seated (a seated part was necessarily\n carried there), so the rungs nest under the seated rungs. Under\n an embodiment with no gripper (the weld contract disabled, e.g.\n robot=\"null\") the grasp state does not exist and the rungs read\n from that implication alone\n gpu_aligned / ram_aligned the part lined up on its slot — origin within `gpu_align_xy` /\n `ram_align_xy` of the seated point, up axis within\n `gpu_align_axis_deg` / `ram_align_axis_deg` of the slot axis,\n length axis within `gpu_align_yaw_deg` / `ram_align_yaw_deg` of\n the slot heading: the scene's own three non-depth seat gates\n gpu_seated / ram_seated depth below the slot mouth (`gpu_slot_mouth_z` / `ram_slot_mouth_z`,\n the scene's own `gpu_engaged()` / `ram_engaged()`) as a fraction\n of `gpu_seat_depth` / `ram_seat_depth`, counted only while\n aligned — so a part's value is 1.0 iff the scene calls it seated\nThe rungs nest (seated => aligned => grasped), so progress reaches 1.0 exactly when the card\nand both sticks are seated, and for a grader built after the picks as well as one that\nwatched them. Success is the scene's own `success()` (card and every stick seated), read when\nthe delivery finishes.", + "grader_summary": "The graphics card and both RAM sticks seated — each pressed to depth, centred, upright, heading along its slot.\n\nLadder (total weight 9, every part worth 3/9):\ncard grasped 1/9 · card aligned 2/9 · card seated 3/9 (0.333) ·\nthen per stick +1/9 per rung: first stick seated 6/9 (0.667) · both sticks seated 1.00.\nEach part's seated rung is a linear ramp of depth from its slot mouth to its seat depth.", + "success_source": "def check_success(self):\n return self.scene.success()", + "grader_source_path": "robobench/suites/assembly/grader/pc_gpu_ram_assembly.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/assembly/grader/pc_gpu_ram_assembly.py", + "gallery": { + "path": "media/pc_gpu_ram/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/pc_gpu_ram.mp4", + "website_playback_rate": 2.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "014f476acf2f933a70997d8bcead427d47ce886d0cf1569f0a7b8be331f5d31f", + "bytes": 809933 + }, + "dataset_source": { + "repo_id": "EmbodiedSWE/pc_gpu_ram_franka_osc.jointpos_15hz_20260907", + "revision": "29f0af1fa2c33b3572f6b80c44a7eb4ed2dd4585", + "format": "v3.0", + "fps": 15, + "total_episodes": 600, + "camera_order": [ + "front", + "high", + "wrist" + ], + "task_index_scope": "local to this source repository; not a benchmark-global task ID", + "robot_type": "assembly.pc_gpu_ram.franka.osc", + "selected_episodes": [ + 0, + 16, + 84 + ], + "source_license_metadata": "license: apache-2.0\ntask_categories:\n- robotics\ntags:\n- LeRobot\n- franka\n- assembly\n- pc-gpu-ram\n- isaac-sim\n- cosigen\n- sim\n- data-gen\nconfigs:\n- config_name: default\n data_files: data/*/*.parquet" + } + }, + { + "catalog_number": 8, + "scene_key": "pc_motherboard", + "official_scene_id": "assembly.pc_motherboard", + "category": "Assembly", + "name": "Motherboard mount", + "instruction": "Use an Allen key to drive seven mounting bolts and secure a motherboard inside a case.", + "difficulty": "Medium", + "catalog_embodiments": "Franka / xArm7 / Gen3", + "rubric": [ + { + "stage": "key_grasped", + "weight": 1, + "normalized_weight": 0.125, + "mode": "once" + }, + { + "stage": "driven", + "weight": 7, + "normalized_weight": 0.875, + "mode": "live" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the pc_motherboard scene: all seven case-mount bolts driven home with the key.\n\nRubric stages (weights and modes live in RUBRIC):\n key_grasped the key has been taken in the weld-on-closure grip (`scene.grasp_held`, whose\n only site is the key handle) — the scene's own grasp state; a milestone,\n latched. Also true once every bolt is seated (a seated bolt was necessarily\n driven by the key), so the rung nests under `driven`. Under an embodiment\n with no gripper (the weld contract disabled, e.g. robot=\"null\") the grasp\n state does not exist and the rung reads from that implication alone\n driven mean over the seven bolts of the tip's depth from the start register to the\n seat — `stage_depth` -> `seat_depth` under the screw mechanic (every bolt\n spawns hand-started that deep), board face (depth 0) -> `seat_depth` when\n the mechanic is off and a loose bolt is threaded in from the top — counted\n only while the bolt is within `align_xy` of its nearest hole axis and\n `align_axis_deg` of upright, the scene's own non-depth seat gates, so a\n bolt's value is 1.0 iff the scene calls it `seated()`\nBoth stages are per env. The bolts spawn pre-staged in their holes, so an \"aligned\" rung\nwould be free at reset and is not one; the key's own \"seated in the hex socket\" step is NOT a\nrung either: the head/socket geometry is not in the scene cfg, so it cannot be read from\nstate without an invented tolerance. Success is the scene's own `success()` (every bolt\n`seated()`), read when the delivery finishes.", + "grader_summary": "All seven motherboard bolts seated — each driven to depth on its hole axis, upright.\n\nLadder (total weight 8, one unit per part — the key and each of the 7 bolts):\nkey grasped 0.125 · 1 bolt 0.25 · 2 bolts 0.375 · 3 bolts 0.50 · 4 bolts 0.625 ·\n5 bolts 0.75 · 6 bolts 0.875 · 7 bolts 1.00. `driven` is the mean over bolts of a linear\nramp of tip depth from the start register (`stage_depth`, or the board face for a loose\nspawn) to `seat_depth`.", + "success_source": "def check_success(self):\n return self.scene.success()", + "grader_source_path": "robobench/suites/assembly/grader/pc_motherboard_assembly.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/assembly/grader/pc_motherboard_assembly.py", + "gallery": { + "path": "media/pc_motherboard/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/pc_motherboard.mp4", + "website_playback_rate": 2.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "60316e66d9158090fd81dbcbb682e4fb3043dc9899f9f55803eae885654c5b73", + "bytes": 379718 + }, + "dataset_source": null + }, + { + "catalog_number": 9, + "scene_key": "pc_ram", + "official_scene_id": "assembly.pc_ram", + "category": "Assembly", + "name": "RAM seating", + "instruction": "Align and seat two memory modules in separate DIMM slots.", + "difficulty": "Easy", + "catalog_embodiments": "Franka / xArm7 / Gen3", + "rubric": [ + { + "stage": "grasped", + "weight": 1, + "normalized_weight": 0.3333333333333333, + "mode": "once" + }, + { + "stage": "aligned", + "weight": 1, + "normalized_weight": 0.3333333333333333, + "mode": "live" + }, + { + "stage": "seated", + "weight": 1, + "normalized_weight": 0.3333333333333333, + "mode": "live" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the pc_ram scene: both RAM sticks seated in their DIMM slots.\n\nThe two sticks are identical sub-goals, so every rung is ONE stage whose value is the\nfraction of sticks (k/2) that have reached it. Per stick the ladder is grasped -> aligned ->\nseated, equal weights:\n grasped sticks ever taken in the weld-on-closure grip (`scene.grasp_held`, one column\n per stick) — the scene's own grasp state; a milestone, latched per stick. Also\n true for a stick that is seated (a seated stick was necessarily carried there),\n so the rung nests under `seated`. Under an embodiment with no gripper (the weld\n contract disabled, e.g. robot=\"null\") the grasp state does not exist and the\n rung reads from that implication alone\n aligned sticks lined up on their slot — origin within `align_xy` of the seated point, up\n axis within `align_axis_deg` of the slot axis, length axis within `align_yaw_deg`\n of the slot heading: the scene's own three non-depth seat gates\n seated mean over sticks of the blade depth below the slot mouth (`slot_mouth_z`, the\n scene's own `engaged()`) as a fraction of `seat_depth`, counted only while\n aligned — so a stick's value is 1.0 iff the scene calls it `seated()`\nThe rungs nest (seated => aligned => grasped), so progress reaches 1.0 exactly when both\nsticks are seated, and for a grader built after the picks as well as one that watched them.\nSuccess is the scene's own `success()` (every stick `seated()`), read when the delivery\nfinishes.", + "grader_summary": "Both sticks seated — pressed to depth, centred, upright, heading along the slot.\n\nLadder (per stick, each rung 1/6): grasped 1/6 · aligned 2/6 · seated 3/6;\none stick done 0.50 · both sticks done 1.00 — three equal rungs for the three steps of\neach stick (take it, line it up on its slot, press it home); the last rung is a linear\nramp of blade depth from the slot mouth (`slot_mouth_z`) to `seat_depth`.", + "success_source": "def check_success(self):\n return self.scene.success()", + "grader_source_path": "robobench/suites/assembly/grader/pc_ram_assembly.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/assembly/grader/pc_ram_assembly.py", + "gallery": { + "path": "media/pc_ram/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/pc_ram.mp4", + "website_playback_rate": 1.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "161ab877358848f66ee4900b4bc1bf3f9dd3589319164bfdaac755ea4b0eaeea", + "bytes": 261521 + }, + "dataset_source": { + "repo_id": "EmbodiedSWE/pc_ram_franka_osc.jointpos_15hz_20260907", + "revision": "d2cd033771eb621a2f35e7e9b9a1c7ab943f5a69", + "format": "v3.0", + "fps": 15, + "total_episodes": 600, + "camera_order": [ + "front", + "wrist" + ], + "task_index_scope": "local to this source repository; not a benchmark-global task ID", + "robot_type": "assembly.pc_ram.franka.osc", + "selected_episodes": [ + 0, + 300, + 599 + ], + "source_license_metadata": "license: apache-2.0\ntask_categories:\n- robotics\ntags:\n- LeRobot\n- franka\n- assembly\n- pc-ram\n- isaac-sim\n- cosigen\n- sim\n- data-gen\nconfigs:\n- config_name: default\n data_files: data/*/*.parquet" + } + }, + { + "catalog_number": 10, + "scene_key": "pen_holder", + "official_scene_id": "packing.pen_holder", + "category": "Organizing", + "name": "Pen holder", + "instruction": "Insert every present pen tip-up and then place the filled holder upright.", + "difficulty": "Easy", + "catalog_embodiments": "Franka / xArm7 / Gen3", + "rubric": [ + { + "stage": "pens_in", + "weight": 0.9, + "normalized_weight": 0.9, + "mode": "live" + }, + { + "stage": "holder_placed", + "weight": 0.1, + "normalized_weight": 0.1, + "mode": "live" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the pen_holder scene: every present pen in the holder tip-up, holder set down upright.\n\nRubric stages (weights and modes live in RUBRIC):\n pens_in fraction of the PRESENT pens the scene counts as in the holder — the\n scene's own `counted()`: bottom within `xy_tol` of the holder axis,\n deeper than `depth_min` below the rim, tip-up within `pen_align_max_deg`,\n holder within `holder_tilt_max_deg` of up, pen and holder settled. All\n pens are one identical family, so they share this one fraction stage;\n the fraction is over the sampled subset (the present pen count is drawn\n per env at reset, k ~ U{min_present..n} per family)\n holder_placed the loaded holder standing upright on the work surface (the scene's\n `holder_placed()`: within `placed_tilt_deg`, bottom within `placed_z_tol`\n of the surface, still), gated on every present pen being in. The holder\n spawns upright on the surface, so ungated it would be free credit at\n reset; the order is fill, then set down\nBoth stages are live: a pen ejected or a holder knocked over loses its credit. Success is\nthe scene's own `success()` — `all_inserted() & holder_placed()` — on the final state.\n\nNot measured: a grasped/lifted rung for the pens. The scene exposes no lift height or hold\ncriterion, and inventing one is forbidden, so that rung is dropped.", + "grader_summary": "Every present pen in the holder tip-up, and the loaded holder standing upright on the surface.\n\nLadder (N present pens): k pens in 0.90 * k / N · holder set down 1.00\n N = 4: 1 pen 0.225 · 2 pens 0.45 · 3 pens 0.675 · 4 pens 0.90 · set down 1.00\n N = 3: 1 pen 0.30 · 2 pens 0.60 · 3 pens 0.90 · set down 1.00", + "success_source": "def check_success(self):\n return self.scene.success()", + "grader_source_path": "robobench/suites/packing/grader/pen_holder.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/packing/grader/pen_holder.py", + "gallery": { + "path": "media/pen_holder/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/pen_holder.mp4", + "website_playback_rate": 1.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "3fb641150508178fbd0900f38122a45364eec92c790e027b142a6254a69466a8", + "bytes": 122980 + }, + "dataset_source": { + "repo_id": "EmbodiedSWE/pen_holder_franka_diffik.jointtarget_15hz_20260909", + "revision": "c3ecaa765252aaca473cccf7c4e770acc5db7f0e", + "format": "v3.0", + "fps": 15, + "total_episodes": 684, + "camera_order": [ + "front", + "corner", + "wrist" + ], + "task_index_scope": "local to this source repository; not a benchmark-global task ID", + "robot_type": "packing.pen_holder.franka.diff_ik", + "selected_episodes": [ + 0, + 342, + 683 + ], + "source_license_metadata": "None" + } + }, + { + "catalog_number": 11, + "scene_key": "tool_packing", + "official_scene_id": "packing.tool_packing", + "category": "Organizing", + "name": "Tool packing", + "instruction": "Stow three tools in their assigned drawers and close the cabinet.", + "difficulty": "Medium", + "catalog_embodiments": "Franka / xArm7 / Gen3", + "rubric": [ + { + "stage": "stowed", + "weight": 0.45, + "normalized_weight": 0.45, + "mode": "live" + }, + { + "stage": "drawer_shut", + "weight": 0.45, + "normalized_weight": 0.45, + "mode": "live" + }, + { + "stage": "cabinet_shut", + "weight": 0.1, + "normalized_weight": 0.1, + "mode": "live" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the tool_packing scene: three tools stowed in their assigned drawers, cabinet shut.\n\nRubric stages (weights and modes live in RUBRIC):\n stowed fraction of the three tools inside their ASSIGNED drawer's tray — the\n scene's own `stowed()`: origin inside the tray-interior box in the drawer's\n body frame (wherever the drawer slid to), settled\n drawer_shut fraction of the three tools that are stowed AND whose own drawer is shut\n (joint within `drawer_closed_tol` of 0) — the second rung of each tool's\n ladder, since every tool has its own drawer to push home\n cabinet_shut the scene's own `success()`: all three stowed, EVERY drawer shut (the\n distractor drawer included), both doors shut on the toolbox variant, and\n items + joints settled\nAll three stages are live: a tool knocked out of its tray or a drawer that slides back open\nloses its credit. Success is the scene's own `success()` on the final state.\n\nNot measured: a grasped/lifted rung for the tools. The scene exposes no lift height or\nhold criterion, and inventing one is forbidden, so that rung is dropped.", + "grader_summary": "All three tools stowed in their assigned drawers and the cabinet fully shut.\n\nLadder: 1 stowed 0.15 · 2 stowed 0.30 · 3 stowed 0.45 · 1 drawer shut 0.60 ·\n 2 drawers shut 0.75 · 3 drawers shut 0.90 · cabinet shut 1.00", + "success_source": "def check_success(self):\n return self.scene.success()", + "grader_source_path": "robobench/suites/packing/grader/tool_packing.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/packing/grader/tool_packing.py", + "gallery": { + "path": "media/tool_packing/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/tool_packing.mp4", + "website_playback_rate": 5.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "b7d769a5d1fd26a206730288507ae77efa806df41a8a630b150f7a60f7214bb2", + "bytes": 708819 + }, + "dataset_source": { + "repo_id": "EmbodiedSWE/tool_packing_franka_diffik.jointtarget_15hz_20260910", + "revision": "b197c503474a91a911e6b8027f8e50cdfe5d135c", + "format": "v3.0", + "fps": 15, + "total_episodes": 1686, + "camera_order": [ + "front", + "corner", + "wrist" + ], + "task_index_scope": "local to this source repository; not a benchmark-global task ID", + "robot_type": "packing.tool_packing.franka.diff_ik", + "selected_episodes": [ + 0, + 843, + 1685 + ], + "source_license_metadata": "None" + } + }, + { + "catalog_number": 12, + "scene_key": "egg_carton", + "official_scene_id": "packing.egg_carton", + "category": "Organizing", + "name": "Egg carton", + "instruction": "Seat three eggs upright in the cells of an egg carton, then push the hinged lid closed.", + "difficulty": "Hard", + "catalog_embodiments": "Unitree G1", + "rubric": [ + { + "stage": "eggs_seated", + "weight": 0.9, + "normalized_weight": 0.9, + "mode": "live" + }, + { + "stage": "lid_closed", + "weight": 0.1, + "normalized_weight": 0.1, + "mode": "live" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the egg_carton scene: `target_eggs` distinct pockets filled with upright eggs, lid closed.\n\nRubric stages (weights and modes live in RUBRIC):\n eggs_seated fraction of the `target_eggs` pockets occupied by a seated egg — the\n scene's own `occupied()`: egg centre within `seat_xy_tol` of a cavity\n centre in the carton frame, in the `seat_z_min..seat_z_max` band, long axis\n within `egg_tilt_max_deg` of the pocket axis, still. Distinct pockets, so a\n second egg dropped into an occupied pocket earns nothing; the eggs are\n identical, so they share this one fraction stage\n lid_closed the scene's own `success()`: every target pocket filled, the lid within\n `lid_closed_deg` of shut (when `require_lid_closed`), eggs and lid settled.\n Gated on the pockets being filled: shutting the lid on an empty carton is not\n progress (it has to be reopened), and the scene's own rubric only awards the\n lid after the eggs\nBoth stages are live: an egg that topples or a lid that swings back open loses its credit.\nSuccess is the scene's own `success()` on the final state.\n\nNot measured: a grasped/lifted rung for the eggs. The scene exposes no lift height or hold\ncriterion, and inventing one is forbidden, so that rung is dropped.", + "grader_summary": "Three eggs seated upright in three distinct carton pockets, then the lid pushed closed.\n\nLadder: 1 egg 0.30 · 2 eggs 0.60 · 3 eggs 0.90 · lid closed 1.00\n(0.90 * k / target_eggs per k pockets filled; the default target is three.)", + "success_source": "def check_success(self):\n return self.scene.success()", + "grader_source_path": "robobench/suites/packing/grader/egg_carton.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/packing/grader/egg_carton.py", + "gallery": { + "path": "media/egg_carton/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/egg_carton.mp4", + "website_playback_rate": 1.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "9243214c2a7895e3fe55fb0aebf0daea1c0069513c03134ccdc5b32f5d376e1d", + "bytes": 1475271 + }, + "dataset_source": null + }, + { + "catalog_number": 13, + "scene_key": "clear_organic_objects", + "official_scene_id": "packing.clear_organic_objects", + "category": "Organizing", + "name": "Clear organics", + "instruction": "Clear the organic objects on a cluttered table into a bin while leaving the other items in place.", + "difficulty": "Medium", + "catalog_embodiments": "Unitree G1", + "rubric": [ + { + "stage": "organics_cleared", + "weight": 1.0, + "normalized_weight": 1.0, + "mode": "live" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the clear_organic_objects scene: every organic in the bin, nothing else in it.\n\nRubric stages (weights and modes live in RUBRIC):\n organics_cleared fraction of the required placements that hold right now — in the\n legacy variant every PRESENT organic cleared into the bin (the scene's\n `cleared()`: origin inside the bin's interior box in the bin's body\n frame, settled); in the sort variant (`sort_to_bin` / `sort_to_tray`)\n the scene's own `bin_targets` cleared plus its `tray_targets` resting\n on the tray. The organics are interchangeable sub-goals, so they share\n this one fraction stage. It is GATED by the two sanity predicates\n below: the whole stage reads 0 while either fails, and recovers when\n the state is repaired (live)\nGates (no weight of their own — rule 7 sanity predicates on the one and only stage, which\nis also the done stage):\n no distractor in the bin the scene's `distractors_in_bin()` — identification IS the\n task: dumping everything in must not score as progress\n bin upright the bin's up-axis within its own topple angle of world-up, the\n angle (from `bin_bbox` x `bin_scale`) past which a free crate's\n centre leaves its base and it falls onto its side. A knocked-\n over bin with produce inside it must not pass. The default bin\n is kinematic and cannot move, so this only bites in the\n dynamic-bin variant\nSuccess is the scene's own `success()` (every required placement holds, no distractor in the\nbin) AND the bin upright, on the final state. `progress == 1` exactly when that holds.\n\nNot measured: a grasped/lifted rung per item (the scene exposes no lift height or hold\ncriterion) and \"bin in place\" (a positional test would need an invented tolerance; the\nbody-frame judging already makes a moved bin judge identically).", + "grader_summary": "Every organic item in the bin, no non-food item in it, the bin still upright.\n\nLadder (N required placements): k placed k / N — e.g. the default manifest's 15 organics:\n1 in 0.067 · 2 in 0.133 · ... · 14 in 0.933 · 15 in 1.00 (a preset with 11 present: 0.091\nper organic); any non-food item in the bin, or the bin tipped over, gates the stage to 0\nuntil repaired.", + "success_source": "def check_success(self):\n return self.scene.success() & self._bin_upright()", + "grader_source_path": "robobench/suites/packing/grader/clear_organic_objects.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/packing/grader/clear_organic_objects.py", + "gallery": { + "path": "media/clear_organic_objects/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/clear_organic.mp4", + "website_playback_rate": 2.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "f2b90694655ae65a2cffb709cefb4f7288ee2adfcd41fd415a02fe7478ea1000", + "bytes": 694445 + }, + "dataset_source": { + "repo_id": "EmbodiedSWE/clear_organic_agent_ab_A400.jointtarget_20hz_20260918", + "revision": "c36f831161a17a6515740cd5c74739e1e79e2e3a", + "format": "v3.0", + "fps": 20, + "total_episodes": 400, + "camera_order": [ + "front", + "side", + "wrist" + ], + "task_index_scope": "local to this source repository; not a benchmark-global task ID", + "robot_type": "packing.clear_organic_objects.franka.joint", + "selected_episodes": [ + 0, + 200, + 399 + ], + "source_license_metadata": "license: apache-2.0\ntask_categories:\n- robotics\ntags:\n- LeRobot\n- robotics\n- franka\n- packing\n- produce\n- pick-and-place\n- isaac-sim\n- cosigen\n- sim\n- agent-ab\nconfigs:\n- config_name: default\n data_files: data/*/*.parquet" + } + }, + { + "catalog_number": 14, + "scene_key": "coffee", + "official_scene_id": "puzzle.coffee", + "category": "Puzzle", + "name": "Coffee brewing", + "instruction": "Load a capsule, brew coffee, and return the filled mug to the serving tray.", + "difficulty": "Medium", + "catalog_embodiments": "Franka / xArm7 / Gen3", + "rubric": [ + { + "stage": "pod_loaded", + "weight": 1, + "normalized_weight": 0.14285714285714285, + "mode": "once" + }, + { + "stage": "bay_closed", + "weight": 1, + "normalized_weight": 0.14285714285714285, + "mode": "once" + }, + { + "stage": "cup_staged", + "weight": 1, + "normalized_weight": 0.14285714285714285, + "mode": "once" + }, + { + "stage": "started", + "weight": 1, + "normalized_weight": 0.14285714285714285, + "mode": "once" + }, + { + "stage": "brewed", + "weight": 1, + "normalized_weight": 0.14285714285714285, + "mode": "live" + }, + { + "stage": "cup_out", + "weight": 1, + "normalized_weight": 0.14285714285714285, + "mode": "once" + }, + { + "stage": "served", + "weight": 1, + "normalized_weight": 0.14285714285714285, + "mode": "live" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the coffee scene: one capsule coffee brewed and the filled mug set back on the tray.\n\nRubric stages (weights and modes live in RUBRIC) — the scene's own seven staged flags\n(`CoffeeServiceScene.STAGES`), in the order the appliance's rules impose:\n pod_loaded the capsule seated in the bay pocket (`pod_seated`)\n bay_closed the cover slid shut over the seated capsule (`pod_seated & cover_closed`)\n cup_staged the mug standing on the platform under the spout (`cup_under_spout`)\n started the machine accepted START — it only does with cover closed, pod seated and\n cup staged (the `started` event of the appliance state machine)\n brewed the brew ran to completion: the machine's latched `filled` flag (an abort on\n early open or on a spill never sets it)\n cup_out the filled mug taken off the platform (`filled & ~cup_under_spout`)\n served the filled mug resting upright, settled, on the tray dish — the scene's own\n `success()`\nThe first six are milestones (a loaded pod, a closed bay or a staged cup need not persist once\nthe brew is done and the mug is carried away); the scene latches them itself at substep rate,\nso the grader reads its flags. `brewed` is the machine's irreversible state, `served` is the\nlive end state. Success is the scene's `success()` on the final state.", + "grader_summary": "The mug filled by a completed brew and resting upright, settled, on the serving tray.\n\nLadder: pod loaded 0.143 · bay closed 0.286 · cup staged 0.429 · started 0.571 ·\n brewed 0.714 · cup out 0.857 · served 1.000\n(uniform: one unit per stage of the appliance's chain, the scene's own even credit).", + "success_source": "def check_success(self):\n return self.scene.success()", + "grader_source_path": "robobench/suites/puzzle/grader/coffee_service.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/puzzle/grader/coffee_service.py", + "gallery": { + "path": "media/coffee/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/coffee.mp4", + "website_playback_rate": 1.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "8286701c28128e716c62730200cb08be95ca6772430f5b51672c54d4353fa4ab", + "bytes": 470402 + }, + "dataset_source": { + "repo_id": "EmbodiedSWE/coffee_franka_joint.jointtarget_60hz_20260907", + "revision": "2450113aeb495c4dd923b72c2a901f1ed84ea884", + "format": "v3.0", + "fps": 60, + "total_episodes": 600, + "camera_order": [ + "front_wide", + "wrist" + ], + "task_index_scope": "local to this source repository; not a benchmark-global task ID", + "robot_type": "puzzle.coffee.franka.joint", + "selected_episodes": [ + 0, + 300, + 599 + ], + "source_license_metadata": "license: apache-2.0\ntask_categories:\n- robotics\ntags:\n- LeRobot\n- franka\n- puzzle\n- coffee\n- isaac-sim\n- cosigen\n- sim\n- data-gen\nconfigs:\n- config_name: default\n data_files: data/*/*.parquet" + } + }, + { + "catalog_number": 15, + "scene_key": "spatula", + "official_scene_id": "puzzle.spatula", + "category": "Puzzle", + "name": "Spatula flip", + "instruction": "Wedge a spatula under bread, flip it, reload it onto the blade, and serve it to a plate.", + "difficulty": "Easy", + "catalog_embodiments": "Franka / xArm7 / Gen3", + "rubric": [ + { + "stage": "lifted", + "weight": 1, + "normalized_weight": 0.2, + "mode": "once" + }, + { + "stage": "wedged", + "weight": 1, + "normalized_weight": 0.2, + "mode": "once" + }, + { + "stage": "flipped", + "weight": 1, + "normalized_weight": 0.2, + "mode": "once" + }, + { + "stage": "loaded", + "weight": 1, + "normalized_weight": 0.2, + "mode": "once" + }, + { + "stage": "served", + "weight": 1, + "normalized_weight": 0.2, + "mode": "live" + } + ], + "rubric_variants": { + "serve": [ + [ + "lifted", + 1, + "once" + ], + [ + "wedged", + 1, + "once" + ], + [ + "loaded", + 1, + "once" + ], + [ + "served", + 1 + ] + ], + "flip": [ + [ + "lifted", + 1, + "once" + ], + [ + "wedged", + 1, + "once" + ], + [ + "flipped_in_pan", + 1 + ] + ], + "flip_serve": [ + [ + "lifted", + 1, + "once" + ], + [ + "wedged", + 1, + "once" + ], + [ + "flipped", + 1, + "once" + ], + [ + "loaded", + 1, + "once" + ], + [ + "served", + 1 + ] + ] + }, + "grader_description": "Grader for the spatula scene: the bread flipped in the pan and/or served onto the plate on the blade.\n\nThe scene's `cfg.goal` picks the chain (its own `_stage_table`); the rubric follows the goal,\nuniform credit per rung:\n serve lifted -> wedged -> loaded -> served\n flip lifted -> wedged -> flipped_in_pan\n flip_serve lifted -> wedged -> flipped -> loaded (after the flip) -> served\n\nRubric stages (weights and modes live in RUBRIC) — the scene's latched stages, read from its\nsubstep-rate latches (the journey), plus the live destination:\n lifted the blade above the surface by `lift_gate` (`_lifted`) — a milestone\n wedged the bread riding the blade while still down at pan-floor level (`_wedged`)\n flipped the bread inverted by >= `flip_min_deg` AND at rest flat in the pan\n (`_flipped`); a milestone in `flip_serve` (the slice is served afterwards)\n loaded the bread riding the blade above `lift_gate` — the friction carry\n (`_loaded`; in `flip_serve` the scene's `_loaded_pf`, the carry AFTER the\n flip, so a load during the flip lift earns nothing)\n served the scene's `success()` for the serve goals: the bread resting flat on the\n plate now, having ARRIVED ON THE BLADE (`_served`, the contact-history\n clause; `flip_serve` also demands the flip latch)\n flipped_in_pan the scene's `success()` for the flip goal: flipped latched AND the bread\n resting inverted, flat and settled in the pan now\nSuccess is the scene's own goal-dependent `success()` on the final state.", + "grader_summary": "The bread flipped in the pan and/or served flat onto the plate (per the scene's goal), carried there on the blade.\n\nLadder (uniform, one unit per rung of the goal's chain):\n flip_serve: lifted 0.20 · wedged 0.40 · flipped 0.60 · loaded after flip 0.80 · served 1.00\n serve: lifted 0.25 · wedged 0.50 · loaded 0.75 · served 1.00\n flip: lifted 0.333 · wedged 0.667 · flipped in pan 1.000", + "success_source": "def check_success(self):\n return self.scene.success()", + "grader_source_path": "robobench/suites/puzzle/grader/spatula_flip_serve.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/puzzle/grader/spatula_flip_serve.py", + "gallery": { + "path": "media/spatula/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/spatula.mp4", + "website_playback_rate": 1.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "42826d4dc25aaf8cf10f81631940e85aa749e65bdbc21a5ce52a772e6f5359b8", + "bytes": 322286 + }, + "dataset_source": null + }, + { + "catalog_number": 16, + "scene_key": "syringe", + "official_scene_id": "puzzle.syringe", + "category": "Liquid", + "name": "Syringe dosing", + "instruction": "Draw liquid from a reservoir, meter three target doses, and repark the syringe.", + "difficulty": "Hard", + "catalog_embodiments": "Bimanual Franka", + "rubric": [ + { + "stage": "drawn", + "weight": 1, + "normalized_weight": 0.2, + "mode": "once" + }, + { + "stage": "dosed", + "weight": 3, + "normalized_weight": 0.6, + "mode": "live" + }, + { + "stage": "parked", + "weight": 1, + "normalized_weight": 0.2, + "mode": "live" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the syringe scene: a full draw, one in-band dose in each of three tubes, syringe laid back down.\n\nRubric stages (weights and modes live in RUBRIC):\n drawn the draw: 1 once the scene's `_drawn_ok` latch is set (liquid >= `draw_min` while\n seated on the reservoir); before that, the liquid in the barrel as a linear ramp\n from empty (0) to `draw_min`. A milestone — the liquid is dispensed afterwards\n dosed fraction over the three identical tubes: a tube inside the scene's `dose_band`\n counts 1; below the band, the dose as a linear ramp from 0 to the band's lower\n edge; pushed past the band it counts 0 — wells never un-fill, so an over-dose is\n the scene's irreversible ruin of that tube. Live: an over-push drops a tube\n parked the scene's `success()`: syringe lying at its home spot, on the shelf, settled\n (`parked`) — GATED on the draw + all three doses. The syringe SPAWNS parked, so\n the park only counts once the dosing it concludes is done (else it is free credit)\nSuccess is the scene's own `success()` (drawn AND doses_ok AND parked) on the final state.", + "grader_summary": "A full draw from the reservoir, an in-band dose in each of the three tubes, and the syringe laid back at its spot.\n\nLadder: full draw 0.20 · 1 dose 0.40 · 2 doses 0.60 · 3 doses 0.80 · parked 1.00", + "success_source": "def check_success(self):\n return self.scene.success()", + "grader_source_path": "robobench/suites/puzzle/grader/syringe_dosing.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/puzzle/grader/syringe_dosing.py", + "gallery": { + "path": "media/syringe/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/syringe.mp4", + "website_playback_rate": 1.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "3325f775d9e26b7e993d18a9805190d58bf6bc88cd40c6e8bfdc1add6cb4dacd", + "bytes": 447660 + }, + "dataset_source": null + }, + { + "catalog_number": 17, + "scene_key": "push_shapes", + "official_scene_id": "puzzle.push_shapes", + "category": "Puzzle", + "name": "Push shapes", + "instruction": "Push three blocks onto their matching pads, correcting each block’s orientation along the way.", + "difficulty": "Easy", + "catalog_embodiments": "Unitree G1", + "rubric": [ + { + "stage": "yaw_aligned", + "weight": 1, + "normalized_weight": 0.3333333333333333, + "mode": "live" + }, + { + "stage": "near_pad", + "weight": 1, + "normalized_weight": 0.3333333333333333, + "mode": "live" + }, + { + "stage": "seated", + "weight": 1, + "normalized_weight": 0.3333333333333333, + "mode": "live" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the push_shapes scene: the T, X and L blocks each pushed into their own cutout.\n\nRubric stages (weights and modes live in RUBRIC). The three blocks are identical sub-goals,\nso every stage is ONE fraction over the env's three blocks (k/3), never a stage per block:\n yaw_aligned blocks whose orientation is inside the scene's coarse yaw band of their pad\n (`yaw_matched`: cfg.yaw_stage_tolerance_deg) — the pivot push is done\n near_pad blocks whose centre is within cfg.near_xy_tolerance of their own pad centre —\n the translation push has arrived at the cutout\n seated blocks seated in their recess AND settled — the scene's own per-piece success\n gate (`seated() & settled()`: xy within cfg.xy_tolerance, orientation within\n cfg.orientation_tolerance_deg, not lifted, dropped into the recess, at rest)\nAll three are LIVE: a block knocked back out of tolerance loses the credit, exactly as the\nscene's own `success()` is a live conjunction. Every rung is implied by the next (7 mm inside\n30 mm, 7 deg inside 10 deg), so a seated block earns all three. Success is the scene's own\n`success()` — all three blocks seated and settled — read on the final state.", + "grader_summary": "All three blocks pushed into their own cutouts — aligned, dropped in, and at rest.\n\nLadder (3 blocks, 3 equal rungs each, 1/9 per rung): per block yaw aligned +1/9 ·\nnear its pad +1/9 · seated +1/9; 1 block seated 0.333 · 2 blocks seated 0.667 ·\n3 blocks seated 1.00.", + "success_source": "def check_success(self):\n return self.scene.success()", + "grader_source_path": "robobench/suites/puzzle/grader/push_shapes.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/puzzle/grader/push_shapes.py", + "gallery": { + "path": "media/push_shapes/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/push_shapes.mp4", + "website_playback_rate": 1.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "8fff7ad71565b5bec705bf662c1c9c473023c56fd8373493a6d12bc28095c89b", + "bytes": 452044 + }, + "dataset_source": null + }, + { + "catalog_number": 18, + "scene_key": "classify_objects", + "official_scene_id": "puzzle.classify_objects", + "category": "Puzzle", + "name": "Classify objects", + "instruction": "Sort scattered coloured blocks into the zones matching their colours.", + "difficulty": "Easy", + "catalog_embodiments": "Unitree G1", + "rubric": [ + { + "stage": "sorted_blocks", + "weight": 1, + "normalized_weight": 1.0, + "mode": "live" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the classify_objects scene: every coloured block sorted into its matching zone.\n\nRubric stages (weights and modes live in RUBRIC):\n sorted_blocks fraction of the env's blocks correctly sorted — the scene's own\n `sorted_mask()`: centre within cfg.zone_half (xy) of ITS OWN category's\n (jittered) zone centre, resting on the surface (cfg.z_tol) and settled\n (cfg.settle_speed)\nThe blocks are identical pick-and-place sub-goals, so they share this ONE fraction stage\n(k/B). It is LIVE: a block knocked off its zone loses the credit, which is what makes the\nfinal state the only one that counts. No grasp/lift rung: the scene defines no lift\nthreshold, so it is dropped rather than invented. Success is the scene's own `success()`\n— every block sorted — read on the final state.", + "grader_summary": "Every block resting, settled, inside the zone of its own colour.\n\nLadder (k / B blocks sorted; B = len(cfg.manifest), 3 by default): 1 block 0.333 ·\n2 blocks 0.667 · 3 blocks 1.00.", + "success_source": "def check_success(self):\n return self.scene.success()", + "grader_source_path": "robobench/suites/puzzle/grader/classify_objects.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/puzzle/grader/classify_objects.py", + "gallery": { + "path": "media/classify_objects/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/classify_objects.mp4", + "website_playback_rate": 1.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "7693b076de061ca463fe56ee170228546abb3bc154abaf53c9462b53337007d0", + "bytes": 443230 + }, + "dataset_source": null + }, + { + "catalog_number": 19, + "scene_key": "stack_blocks", + "official_scene_id": "puzzle.stack_blocks", + "category": "Puzzle", + "name": "Stack blocks", + "instruction": "Stack scattered blocks into a single aligned tower on a marked pad.", + "difficulty": "Medium", + "catalog_embodiments": "Unitree G1", + "rubric": [ + { + "stage": "tower", + "weight": 1, + "normalized_weight": 1.0, + "mode": "live" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the stack_blocks scene: all blocks stacked into one upright tower on the pad.\n\nRubric stages (weights and modes live in RUBRIC):\n tower contiguous tower height in blocks over cfg.n_blocks — the scene's own\n `tower_height()`: slots 0, 1, 2, ... above the pad centre, each filled by a block\n within cfg.align_tol of the pad axis, at the slot height within cfg.z_tol,\n upright within cfg.upright_max_deg, settled with the whole scene settled\n (cfg.settle_speed), counted up to the first empty slot\nThe cubes are identical sub-goals, so they share this ONE fraction stage (k/n_blocks). It is\nLIVE: a toppled tower loses the credit. No grasp/lift rung: the scene defines no lift\nthreshold, so it is dropped rather than invented. Success is the scene's own `success()` —\nevery slot filled by exactly one block — read on the final state. With n_blocks blocks and\nn_blocks slots, a full-height tower (every slot >= 1 block) is exactly one block per slot,\nso the stage reaches 1 if and only if `success()` holds.", + "grader_summary": "One clean tower on the pad — every slot filled by exactly one upright, settled block.\n\nLadder (k / n_blocks tower height; cfg.n_blocks = 4 by default): 1 block 0.25 ·\n2 blocks 0.50 · 3 blocks 0.75 · 4 blocks 1.00.", + "success_source": "def check_success(self):\n return self.scene.success()", + "grader_source_path": "robobench/suites/puzzle/grader/stack_blocks.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/puzzle/grader/stack_blocks.py", + "gallery": { + "path": "media/stack_blocks/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/stack_blocks.mp4", + "website_playback_rate": 1.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "7827adcc799e671f6e55acfdb4bfc620b538751a8bbcaf0b9008aae2eb7c2c5a", + "bytes": 530682 + }, + "dataset_source": null + }, + { + "catalog_number": 20, + "scene_key": "tshirt", + "official_scene_id": "deformable.tshirt", + "category": "Deformable", + "name": "T-shirt folding", + "instruction": "Fold a T-shirt into a compact, flat bundle.", + "difficulty": "Medium", + "catalog_embodiments": "Franka / xArm7 / Gen3", + "rubric": [ + { + "stage": "sleeves", + "weight": 2, + "normalized_weight": 0.4, + "mode": "live" + }, + { + "stage": "hem", + "weight": 1, + "normalized_weight": 0.2, + "mode": "live" + }, + { + "stage": "folded", + "weight": 1, + "normalized_weight": 0.2, + "mode": "live" + }, + { + "stage": "plausible", + "weight": 1, + "normalized_weight": 0.2, + "mode": "final" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the tshirt scene: the shirt folded flat — sleeves to the center line, hem up over the collar.\n\nRubric stages (weights and modes live in RUBRIC):\n sleeves fraction of the two sleeve folds done — for each sleeve, the linear progress of\n its tip particle (the max-x / min-x cloth particle at the graded reset) from its\n reset x to the shirt's center line (x = 0, the table's axis); two identical\n sub-goals, so ONE stage worth two rungs, value k/2\n hem the bottom hem's edge particle (max-y at reset) carried from its reset y to the\n collar's reset y (min-y at reset): linear, 1 at or past the collar\n folded the scene's own success: footprint (x-extent · y-extent of the cloth) at or\n below `cfg.fold_footprint_max`\n plausible the VLM plausibility gate on the rendered final frame (final rung): a neat flat\n compact bundle on the table — rejects crumpled/balled cloth, cloth hanging off\n the table, cloth still spread out\nEvery stage is per env. Success = the scene's `success()` (footprint gate) on the final state\nAND the plausibility gate — the gate can only take credit away.\n\nNot measurable from state (hence the gate): whether the small footprint is a fold or a crumple,\nand whether the cloth rests on the table rather than hanging off it — the scene has no such\npredicates and inventing tolerances is not allowed.", + "grader_summary": "The T-shirt folded into a flat compact bundle on the table (footprint gate), judged plausible.\n\nLadder: 1 sleeve 0.20 · 2 sleeves 0.40 · hem over collar 0.60 · folded (footprint) 0.80 · gate 1.00", + "success_source": "def check_success(self):\n return self.scene.success()", + "grader_source_path": "robobench/suites/deformable/grader/tshirt.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/deformable/grader/tshirt.py", + "gallery": { + "path": "media/tshirt/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/tshirt.mp4", + "website_playback_rate": 2.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "833f6a1dd107ca3404eec7324c2e8fef5d422e317698b852eb4f765c71982c29", + "bytes": 699184 + }, + "dataset_source": null + }, + { + "catalog_number": 21, + "scene_key": "latte", + "official_scene_id": "deformable.latte", + "category": "Liquid", + "name": "Latte pouring", + "instruction": "Coordinate two arms to pour milk into a carried coffee mug without excessive spilling.", + "difficulty": "Hard", + "catalog_embodiments": "Bimanual Franka", + "rubric": [ + { + "stage": "pitcher_grasped", + "weight": 1, + "normalized_weight": 0.25, + "mode": "once" + }, + { + "stage": "transferred", + "weight": 1, + "normalized_weight": 0.25, + "mode": "live" + }, + { + "stage": "poured", + "weight": 1, + "normalized_weight": 0.25, + "mode": "live" + }, + { + "stage": "plausible", + "weight": 1, + "normalized_weight": 0.25, + "mode": "final" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the latte scene: milk poured into the coffee without emptying the pitcher or spilling.\n\nRubric stages (weights and modes live in RUBRIC):\n pitcher_grasped the pitcher taken — the scene's own auto-weld grasp contract engaged on the\n pitcher's handle bar by either hand (`_auto_state` of `weld_pitcher` /\n `weld_pitcher_l`); a milestone, credit kept after the release\n transferred linear ramp of the scene's `transfer_fraction()` (milk particles inside the\n mug) from 0 to `cfg.success_transfer_min`\n poured the scene's own success: all four gates at once — milk transferred\n (>= success_transfer_min), milk kept in the pitcher (>= success_kept_min),\n coffee retained (>= success_retention_min), nothing spilled\n (<= success_spilled_max). The three guards are sanity predicates on the done\n rung (a live stage that is true from the first step would score a run that\n did nothing), not weighted rungs of their own\n plausible the VLM plausibility gate on the rendered final frame (final rung): two\n upright vessels resting on the table, coffee with milk poured in — rejects\n the pitcher inside/on the mug, tipped vessels, vessels held mid-air or off\n the table, liquid pooled on the table\nEvery stage is per env (the MPM scene is single-env; scalars broadcast). Success = the scene's\n`success()` on the final state AND the plausibility gate.\n\nNot measurable from state (hence the gate): a pitcher dunked into the mug passes every particle\nmetric; \"upright and resting on the table\" has no scene predicate, and inventing a tilt/height\ntolerance is not allowed.", + "grader_summary": "Milk poured into the coffee mug — the scene's four gates met — with both vessels left upright on the table.\n\nLadder: pitcher grasped 0.25 · milk transferred 0.50 · poured (4 gates) 0.75 · gate 1.00", + "success_source": "def check_success(self):\n return self.scene.success()", + "grader_source_path": "robobench/suites/deformable/grader/latte.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/deformable/grader/latte.py", + "gallery": { + "path": "media/latte/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/latte.mp4", + "website_playback_rate": 3.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "0c2d5974441e189c3a1764f553509ebe868b7c41e66558dc04f6436909281003", + "bytes": 811918 + }, + "dataset_source": null + }, + { + "catalog_number": 22, + "scene_key": "dumpling", + "official_scene_id": "deformable.dumpling", + "category": "Deformable", + "name": "Dough rolling", + "instruction": "Roll a ball of dough flat into a round dumpling wrapper with a rolling pin.", + "difficulty": "Hard", + "catalog_embodiments": "Franka / xArm7 / Gen3", + "rubric": [ + { + "stage": "flattened", + "weight": 1, + "normalized_weight": 0.25, + "mode": "live" + }, + { + "stage": "spread", + "weight": 1, + "normalized_weight": 0.25, + "mode": "live" + }, + { + "stage": "rolled", + "weight": 1, + "normalized_weight": 0.25, + "mode": "live" + }, + { + "stage": "plausible", + "weight": 1, + "normalized_weight": 0.25, + "mode": "final" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the dumpling scene: the dough ball rolled out into a thin, wide, compact wrapper.\n\nRubric stages (weights and modes live in RUBRIC):\n flattened linear ramp of the scene's `flatten_height()` (q95 dough height above the table)\n from its value at the graded reset (the ball, ~0.044 m) down to\n `cfg.flatten_h95_max`\n spread linear ramp of the scene's `spread_radius()` (q90 radius about the dough centroid)\n from its reset value (~0.020 m) up to `cfg.spread_r90_min`\n rolled the scene's own success: thin (flatten_h95_max) AND wide (spread_r90_min) AND the\n guards — compact (`final_extent() <= extent_max`), conserved\n (`conservation() >= conserve_min`), nothing punched through the table\n (`min_particle_z() >= surface_z - floor_z_slack`). The guards are sanity predicates\n on the done rung, not weighted rungs (they hold from the first step)\n plausible the VLM plausibility gate on the rendered final frame (final rung): one round,\n flat, even wrapper on the board with the pin set aside — rejects splats and\n irregular shapes, torn/multiple pieces, dough on the pin or the pin in the dough,\n dough off the board\nEvery stage is per env (the MPM scene is single-env). Success = the scene's `success()` on the\nfinal state AND the plausibility gate.\n\nNo pin-grasp rung: the scene's success does not involve the pin (dough flattened by any means\npasses), so a grasp milestone could leave progress below 1 on an official success.\nNot measurable from state (hence the gate): roundness/evenness of the sheet and whether the pin\nis set aside — thin + wide + compact is also satisfied by a splat hammered with the pin's end.", + "grader_summary": "The dough rolled out into a thin, wide, compact wrapper on the board — the scene's gates met — judged plausible.\n\nLadder: flattened 0.25 · spread 0.50 · rolled (scene gates) 0.75 · gate 1.00", + "success_source": "def check_success(self):\n return self.scene.success()", + "grader_source_path": "robobench/suites/deformable/grader/dumpling.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/deformable/grader/dumpling.py", + "gallery": { + "path": "media/dumpling/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/dumpling.mp4", + "website_playback_rate": 1.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "5add18c9c44c41b506eedae251f2305ec51a55798f21de9fa7f35138f74c7077", + "bytes": 577626 + }, + "dataset_source": null + }, + { + "catalog_number": 23, + "scene_key": "shoe_knot", + "official_scene_id": "deformable.shoe_knot", + "category": "Deformable", + "name": "Shoelace half-knot", + "instruction": "Tie a half knot from the two shoelaces of a sneaker.", + "difficulty": "Hard", + "catalog_embodiments": "Franka / xArm7 / Gen3", + "rubric": [ + { + "stage": "wound", + "weight": 1, + "normalized_weight": 0.25, + "mode": "live" + }, + { + "stage": "snug", + "weight": 1, + "normalized_weight": 0.25, + "mode": "live" + }, + { + "stage": "tied", + "weight": 1, + "normalized_weight": 0.25, + "mode": "live" + }, + { + "stage": "plausible", + "weight": 1, + "normalized_weight": 0.25, + "mode": "final" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the knot scene: a self-holding half knot tied from the two laces, seated on the tongue.\n\nRubric stages (weights and modes live in RUBRIC):\n wound linear ramp of the scene's `knot_winding()` (lace 1's winding around lace 2 on the\n knot sections, degrees) from 0 to `cfg.winding_min_deg`\n snug linear ramp of the scene's `knot_contacts()` (cross-lace contact pairs on the knot\n sections) from 0 to `cfg.contacts_min`\n tied the scene's own `success(world)`: winding >= winding_min_deg AND contacts >=\n contacts_min AND the knot (closest cross-lace pair) seated at task-local\n z < `cfg.knot_z_max`. The seat gate is a sanity predicate on this done rung, not a\n rung of its own: with the laces at rest the closest cross-lace pair already sits\n at z ~ 0.12 < 0.155, so a standalone height rung would score a run that did nothing\n plausible the VLM plausibility gate on the rendered final frame (final rung): a single half\n knot on the tongue, shoe upright and in place — rejects laces wrapped around the\n shoe, shoe moved/flipped, laces merely crossed or lying loose, ends tied around\n anything else\nThe scene's metrics are numpy, per world: every stage loops over the worlds and returns a\n`(num_envs,)` tensor. Success = the scene's `success(world)` on the final state AND the\nplausibility gate.\n\nNot measurable from state (hence the gate): the scene exposes no slack/hold check (the smoke's\ncheck window is smoke logic, not a scene predicate) and no \"knot is around lace 2, not the shoe\"\ntest — winding + contacts are also satisfied by laces wound around the shoe body.", + "grader_summary": "A self-holding half knot tied from the two laces and seated on the tongue — the scene's gates met — judged plausible.\n\nLadder: wound 0.25 · snug 0.50 · tied (scene gates) 0.75 · gate 1.00", + "success_source": "def check_success(self):\n import torch\n\n return torch.tensor([bool(self.scene.success(w)) for w in self._worlds()])", + "grader_source_path": "robobench/suites/deformable/grader/shoe_knot.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/deformable/grader/shoe_knot.py", + "gallery": null, + "dataset_source": null + }, + { + "catalog_number": 24, + "scene_key": "slice", + "official_scene_id": "cutting.slice", + "category": "Cutting", + "name": "Banana slicing", + "instruction": "Pick up a chef knife from its rest and slice a carrot (or a banana) into a target number of pieces on a chopping board.", + "difficulty": "Easy", + "catalog_embodiments": "Franka / xArm7 / Gen3", + "rubric": [ + { + "stage": "knife_taken", + "weight": 1, + "normalized_weight": 0.1111111111111111, + "mode": "once" + }, + { + "stage": "planes_cut", + "weight": 7, + "normalized_weight": 0.7777777777777778, + "mode": "live" + }, + { + "stage": "sliced", + "weight": 1, + "normalized_weight": 0.1111111111111111, + "mode": "live" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the slice scene: the food cut into its target number of pieces, all still on the board.\n\nRubric stages (weights and modes live in RUBRIC):\n knife_taken the knife has been lifted clear of its rest — its root raised by at least\n cfg.rest_rail_h (the rails' height) above where it lay at the graded reset, so\n the edge is above the rail tops and the notches no longer hold it; a milestone\n (\"once\"), credit kept after the knife is set down again\n planes_cut fraction of the cuts the target needs that have been made — released planes\n over (target - 1), a linear ramp from the welded food (0) to the target piece\n count (1), target = cfg.target_pieces or every piece. The scored planes are\n identical sub-goals: one stage, one fraction, each plane worth the same\n sliced the scene's own done test: piece count (connected components of the live weld\n chain, `pieces_count()`) reaches the target AND every piece is still on the\n chopping board — inside the board's footprint (cfg.board_size) and above its\n top. \"On the board\" is a GATE on this stage, not a stage of its own: it is\n true at reset and only means anything once the food is cut\nSuccess = `sliced` read when the delivery finishes.", + "grader_summary": "Food sliced into the target number of pieces, every piece still on the board.\n\nLadder (7 scored planes — the carrot / banana assets — target = all pieces): knife taken\n1/9 (0.111) · each plane cut +1/9 (7 planes -> 8/9 = 0.889) · sliced with every piece on\nthe board 1.00.", + "success_source": "def check_success(self):\n return self._sliced()", + "grader_source_path": "robobench/suites/cutting/grader/slice_food.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/cutting/grader/slice_food.py", + "gallery": { + "path": "media/slice/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/slice_banana.mp4", + "website_playback_rate": 1.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "26ce8e19831ee420a2bb8dd74e46d90ac1e8e6b832e6c65fbc777a216b8fb547", + "bytes": 399060 + }, + "dataset_source": { + "repo_id": "EmbodiedSWE/cutting_slice_franka_diffik.jointtarget_15hz_train_20260919", + "revision": "0b89ef8fd191fd09815e7edb8e5fcb467f95e494", + "format": "v3.0", + "fps": 15, + "total_episodes": 4198, + "camera_order": [ + "front", + "wrist" + ], + "task_index_scope": "local to this source repository; not a benchmark-global task ID", + "robot_type": "cutting.slice_banana.franka.diff_ik", + "selected_episodes": [ + 0, + 1055, + 3664 + ], + "source_license_metadata": "None" + } + }, + { + "catalog_number": 25, + "scene_key": "dice", + "official_scene_id": "cutting.dice", + "category": "Cutting", + "name": "Food dicing", + "instruction": "Dice a tomato (or a potato) into a 3×3 grid of pieces, cutting along two plane families with the knife re-yawed between them.", + "difficulty": "Medium", + "catalog_embodiments": "Franka / xArm7 / Gen3", + "rubric": [ + { + "stage": "knife_taken", + "weight": 1, + "normalized_weight": 0.07142857142857142, + "mode": "once" + }, + { + "stage": "pairs_cut", + "weight": 12, + "normalized_weight": 0.8571428571428571, + "mode": "live" + }, + { + "stage": "diced", + "weight": 1, + "normalized_weight": 0.07142857142857142, + "mode": "live" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the dice scene: the food diced into its target number of pieces, all still on the board.\n\nRubric stages (weights and modes live in RUBRIC):\n knife_taken the knife has been lifted off its rest — its root raised by the rest's rail\n height (`cfg.rest_rail_h`), the lift that frees the plate from the notches; a\n milestone, credit kept after the knife is put down\n pairs_cut fraction of the grid's weld PAIRS released (`scene.pair_cut`, the dice gate's\n own truth). The x-planes and y-planes are identical sub-goals — one stage, one\n fraction, each pair worth the same\n diced the scene's own done test: connected components of the live weld graph reach\n the target piece count AND every piece is still on the board (footprint from\n `cfg.board_size`, resting above the board top). \"On the board\" is a GATE on\n this stage, not a stage of its own — it is true at reset and only means\n anything once the food is diced\nSuccess = `diced` read on the final state.", + "grader_summary": "Food diced into the target number of pieces, every piece still on the board.\n\nLadder: knife taken 1/14 (0.071) · each weld pair released +1/14 (12 pairs -> 13/14 =\n0.929) · diced with every piece on the board 1.00", + "success_source": "def check_success(self):\n return self._diced()", + "grader_source_path": "robobench/suites/cutting/grader/dice_food.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/cutting/grader/dice_food.py", + "gallery": null, + "dataset_source": null + }, + { + "catalog_number": 26, + "scene_key": "fruit_delivery", + "official_scene_id": "locomanip.fruit_delivery", + "category": "Locomanip", + "name": "Fruit delivery", + "instruction": "Pick up every fruit on a long table and carry it around the table to a plate beyond arm reach.", + "difficulty": "Medium", + "catalog_embodiments": "Unitree G1", + "rubric": [ + { + "stage": "lifted", + "weight": 1, + "normalized_weight": 0.3333333333333333, + "mode": "once" + }, + { + "stage": "carried", + "weight": 1, + "normalized_weight": 0.3333333333333333, + "mode": "once" + }, + { + "stage": "placed", + "weight": 1, + "normalized_weight": 0.3333333333333333, + "mode": "live" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the fruit delivery scene: every fruit carried around the table and set on the far plate.\n\nRubric stages (weights and modes live in RUBRIC), each a fraction over the fruits PRESENT this\nepisode (the scene's `present` mask; identical fruits share one stage):\n lifted fraction of fruits ever raised clear of the table — the fruit's origin (the produce\n assets sit with their origin at the base, on `cfg.surface_z`) above the plate's rim\n height (`cfg.plate_rim_z`), the height every fruit must clear to enter the dish; a\n milestone, credit kept after the fruit is set down\n carried mean transit progress: for each fruit, a linear ramp of its radial xy distance from\n the plate's counted centre (the scene's own body-frame measure, see `_on_plate`) from\n where it lay at the graded reset (0) to the counted footprint edge\n `cfg.radius_frac * cfg.plate_radius` (1); a milestone, so a fruit that arrives and is\n released keeps the credit\n placed the scene's own `fruits_placed()` — on the plate AND settled — as a fraction of the\n present fruits, GATED by the scene's `nonfruits_on_plate()` being empty: a pumpkin or\n onion on the plate fails the task, so it zeroes the done stage rather than being a\n weighted stage of its own (it is trivially \"true\" at reset)\nSuccess is the scene's own `success()`: every present fruit placed and nothing that is not a fruit\non the plate, read on the final state.", + "grader_summary": "Every fruit carried around the table and resting on the far-side plate, nothing else put on it.\n\nLadder (one fruit): lifted 0.333 · carried 0.667 · placed 1.00\nWith N fruits every rung is the fraction over the N present fruits, so k fruits fully\ndelivered score k/N.", + "success_source": "def check_success(self):\n return self.scene.success()", + "grader_source_path": "robobench/suites/locomanip/grader/fruit_delivery.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/locomanip/grader/fruit_delivery.py", + "gallery": { + "path": "media/fruit_delivery/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/fruit_delivery.mp4", + "website_playback_rate": 1.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "e6d35cb7c44c87f2c5c1fbefca70977021f2e96429da66646c3500ad3c5bbaea", + "bytes": 328419 + }, + "dataset_source": null + }, + { + "catalog_number": 27, + "scene_key": "box_to_bin", + "official_scene_id": "locomanip.box_to_bin", + "category": "Locomanip", + "name": "Box to bin", + "instruction": "Pick a cardboard box off a shelf and carry it to a sorting bin on a table across the room.", + "difficulty": "Medium", + "catalog_embodiments": "Unitree G1", + "rubric": [ + { + "stage": "picked", + "weight": 1, + "normalized_weight": 0.3333333333333333, + "mode": "once" + }, + { + "stage": "carried", + "weight": 1, + "normalized_weight": 0.3333333333333333, + "mode": "once" + }, + { + "stage": "in_bin", + "weight": 1, + "normalized_weight": 0.3333333333333333, + "mode": "live" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the box-to-bin scene: the box taken off the shelf, walked around the corner, and left in the bin.\n\nRubric stages (weights and modes live in RUBRIC) — the scene's own chain lifted -> transited ->\nplaced, one rung each:\n picked the scene's `lifted` latch: the box got clear of its shelf board, either raised\n `cfg.lift_h` above its resting height or seen held up in the apron in front of the\n shelf face (both pick styles the open shelving affords); a milestone\n carried the scene's own `carried_fraction()` — the fraction of the start-to-bin xy distance\n the box has closed, a linear ramp — reading 1 once the scene's `transited` latch has\n fired (its own \"the transit is done\" mark, which `success()` requires); a milestone,\n so a box that arrives and is released keeps the credit\n in_bin the scene's own `placed()` — fully inside the bin's inset interior bands, below the\n rim-excluding height cap, and settled; live, so it dips if the box leaves the bin\nSuccess is the scene's own `success()` — `lifted & transited & placed()` — read on the final\nstate; the three rungs are exactly its three conjuncts, so progress reads 1.0 iff it holds.", + "grader_summary": "The cardboard box picked off the shelf, carried around the corner, and resting inside the sorting bin.\n\nLadder: picked 0.333 · carried 0.667 (linear in the distance closed) · in bin 1.00", + "success_source": "def check_success(self):\n return self.scene.success()", + "grader_source_path": "robobench/suites/locomanip/grader/box_to_bin.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/locomanip/grader/box_to_bin.py", + "gallery": { + "path": "media/box_to_bin/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/box_to_bin.mp4", + "website_playback_rate": 1.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "7819414f4155c0e5f90b9f25ab4aa07dbfaacb05f10872e96796c7cf3417bb3a", + "bytes": 175299 + }, + "dataset_source": null + }, + { + "catalog_number": 28, + "scene_key": "wheel_carry", + "official_scene_id": "locomanip.wheel_carry", + "category": "Locomanip", + "name": "Wheel carry", + "instruction": "Pick a steering wheel off one packing table and carry it to a basket on another, three metres away.", + "difficulty": "Medium", + "catalog_embodiments": "Unitree G1", + "rubric": [ + { + "stage": "picked", + "weight": 1, + "normalized_weight": 0.3333333333333333, + "mode": "once" + }, + { + "stage": "carried", + "weight": 1, + "normalized_weight": 0.3333333333333333, + "mode": "once" + }, + { + "stage": "in_basket", + "weight": 1, + "normalized_weight": 0.3333333333333333, + "mode": "live" + } + ], + "rubric_variants": null, + "grader_description": "Grader for the wheel carry scene: the wheel picked up here, walked over there, and left in the basket.\n\nRubric stages (weights and modes live in RUBRIC) — the scene's own chain lifted -> transited ->\nplaced, one rung each:\n picked the scene's `lifted` latch: the wheel's origin raised `cfg.lift_h` above the pick\n table top; a milestone, credit kept after it is put down\n carried the scene's own `carried_fraction()` — the fraction of `cfg.carry_dx` the wheel\n has travelled along +x from the pick station, a linear ramp — reading 1 once the\n scene's `transited` latch has fired (its own \"the transit is done\" mark, which\n `success()` requires); a milestone, so a wheel that arrives and is released keeps\n the credit\n in_basket the scene's own `placed()` — inside the basket's target bands, below the height\n cap, and settled; live, so it dips if the wheel leaves the basket\nSuccess is the scene's own `success()` — `lifted & transited & placed()` — read on the final\nstate; the three rungs are exactly its three conjuncts, so progress reads 1.0 iff it holds.", + "grader_summary": "The steering wheel lifted off the near table, carried across, and resting inside the far basket.\n\nLadder: picked 0.333 · carried 0.667 (linear in the distance closed) · in basket 1.00", + "success_source": "def check_success(self):\n return self.scene.success()", + "grader_source_path": "robobench/suites/locomanip/grader/wheel_carry.py", + "grader_url": "https://github.com/EmbodiedSWE/EmbodiedSWE/blob/433d0ceedc2c5acf58551f4b3502b2cd1c7e8f1b/robobench/suites/locomanip/grader/wheel_carry.py", + "gallery": { + "path": "media/wheel_carry/website_demo.mp4", + "url": "https://embodiedswe.github.io/assets/video/wheel_carry.mp4", + "website_playback_rate": 1.0, + "camera_label": "WEBSITE RENDER VIEW", + "sha256": "79805b9c731ab40666c5c1abe55203257880e0d15d72656125a0f5dd1bd6514a", + "bytes": 367523 + }, + "dataset_source": null + } +] \ No newline at end of file diff --git a/provenance/validation.json b/provenance/validation.json new file mode 100644 index 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"codec_name": "h264", + "width": 1280, + "height": 720, + "duration": "8.333333", + "nb_frames": "200" + }, + { + "scene_key": "latte", + "file": "media/latte/website_demo.mp4", + "codec_name": "h264", + "width": 1280, + "height": 720, + "duration": "12.083008", + "nb_frames": "290" + }, + { + "scene_key": "dumpling", + "file": "media/dumpling/website_demo.mp4", + "codec_name": "h264", + "width": 1280, + "height": 720, + "duration": "9.125000", + "nb_frames": "219" + }, + { + "scene_key": "slice", + "file": "media/slice/website_demo.mp4", + "codec_name": "h264", + "width": 1280, + "height": 720, + "duration": "5.875000", + "nb_frames": "141" + }, + { + "scene_key": "fruit_delivery", + "file": "media/fruit_delivery/website_demo.mp4", + "codec_name": "h264", + "width": 1280, + "height": 720, + "duration": "5.791667", + "nb_frames": "139" + }, + { + "scene_key": "box_to_bin", + "file": "media/box_to_bin/website_demo.mp4", + "codec_name": "h264", + "width": 1280, + "height": 720, + "duration": "10.000000", + "nb_frames": "240" + }, + { + "scene_key": "wheel_carry", + "file": "media/wheel_carry/website_demo.mp4", + "codec_name": "h264", + "width": 1280, + "height": 720, + "duration": "4.750000", + "nb_frames": "114" + } +] \ No newline at end of file diff --git a/reference/LICENSE b/reference/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..29f81d812f3e768fa89638d1f72920dbfd1413a8 --- /dev/null +++ b/reference/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/reference/README.md b/reference/README.md new file mode 100644 index 0000000000000000000000000000000000000000..94014347bceb7d08c1a339bbe3bacd7d137e96ab --- /dev/null +++ b/reference/README.md @@ -0,0 +1,166 @@ +# EmbodiedSWE: Coding Agents for Long-Horizon Dexterous Robotics + +

+ EmbodiedSWE overview +

+ +

+ arXiv + Project page + Blog +

+ +EmbodiedSWE studies how frontier coding agents can help robotics. It has four parts: + +- **EmbodiedSWE-Bench**, an agent-native benchmark of long-horizon, dexterous everyday tasks, built on Isaac Lab. +- **Evaluation** of frontier coding agents on these tasks: task performance, completion time, and inference cost. +- **EmbodiedSWE-Gen**, which diversifies one verified agent solution into a large trajectory dataset for training general robot policies. +- **Agent improvement**, which generates new tasks from existing ones and improves the coding agent with RL on verified outcomes. + +See the [paper](https://arxiv.org/abs/2609.27308) for the full benchmark, evaluation, and data-generation details. + +> **Note:** this repository is under active development. Folder layouts, interfaces, and settings +> may change as the codebase evolves. + +## Installation + +Requirements: Linux, an NVIDIA GPU with a CUDA 12.x driver, and [`uv`](https://docs.astral.sh/uv/). + +```bash +git clone && cd +./scripts/bootstrap_isaaclab_5_1.sh # Isaac Sim 5.1 + Isaac Lab 2.3.2 + robobench into ./.venv +source .venv/bin/activate +export OMNI_KIT_ACCEPT_EULA=YES # Isaac Sim asks interactively otherwise (hangs headless runs) +# Optional: prefetch every task and room (~3.4 GB); otherwise env.build() fetches what it needs. +python -m robobench.scripts.fetch_assets +uv pip install "pin==2.7.0" "pin-pink==3.1.0" "daqp==0.8.5" "numpy==1.26.0" # whole-body IK (pink_ik) +``` + +Task assets and shared rooms live in the Hugging Face dataset `EmbodiedSWE/robobench-assets`. +Building an environment automatically downloads its required asset groups and verifies their SHA-256 +checksums against `robobench/assets_manifest.json`. Verified local copies are reused, including offline. +Listing tasks does not download anything. `fetch_assets --check` verifies the whole local collection. + +Every built-in registered task configuration declares its default room in its suite's +`configs/envs.py`, including alternate robots and control modes. Shared loading code lives in +`robobench/core/rooms.py`; downloaded rooms live in the ignored `robobench/assets/rooms/` directory. +Use `--room none` with the smoke launcher or `cfg.build(room=None)` to disable scenery explicitly. +Set `COSIGEN_ASSET_DIR` before starting Python to use a writable cache outside the checkout. +See [task rooms and asset downloads](docs/rooms.md) for the room assignments and maintainer commands. + +The `deformable` suite runs on the Newton physics backend and needs a separate venv; see +[`robobench/suites/deformable/README.md`](robobench/suites/deformable/README.md). + +## Quick start + +Preview a task: + +```bash +python -m robobench.scripts.smoke --list # all registered tasks +python -m robobench.scripts.smoke --env assembly.bulb.franka.osc --livestream 2 # random actions, live view +``` + +Tasks are named `suite.scene[.robot[.control_mode]]`. The environment API and design are described +in [`robobench/README.md`](robobench/README.md). + +**A few examples from EmbodiedSWE-Bench:** + + + + + + + + + + + + + + + + +

assembly.bulb
assembly.ikea_table
assembly.so101
assembly.pc_motherboard
packing.tool_packing

packing.egg_carton
deformable.tshirt
deformable.latte
cutting.slice
locomanip.fruit_delivery
+ +## Solving a task + +The easy way: open the repo in a coding agent such as Claude Code or Codex, point it at +`robobench/README.md`, and ask it to solve a task, e.g. `assembly.bulb.franka.osc`. A solution is a +Python program exposing `solve(env)`; the contract the agents see is +[`eval/prompts/_contract.md`](eval/prompts/_contract.md). + +For rigorous, large-scale evaluation, `eval/` runs the agent in an isolated Docker container under a +time budget and grades its solutions afterwards in fresh containers on independently randomized episodes. + +```bash +python eval/scripts/build_env.py --name bulb_e2e --stage bulb:franka # build the world once +python eval/scripts/run_agent.py experiments/bulb_e2e --agent claude # one agent run in Docker +python eval/scripts/run_grade.py experiments/bulb_e2e --run # grade the delivery +``` + +Prompt conditions (hints, rules, blocked features) are authored yaml files in `eval/configs/` and +`eval/prompts/`. Docker images and the container contract are documented in +[`eval/docker/README.md`](eval/docker/README.md). + +## Data engine + +Once a task is solved, `data_engine/` turns that one verified solution into a large, diverse, +per-episode-verified demonstration dataset. Coding agents author the diversity at five independent +levels: scene, strategy, phase, dynamics, and visual. + +A generic launcher then mass-produces batched episodes, the scene's grader stamps a verdict on each +one, and the verified episodes are rendered and baked into a LeRobot dataset for policy training. + +```bash +python data_engine/scripts/init_gen.py experiments/bulb_e2e/runs/ # start a campaign from a solved run +python data_engine/scripts/diversify.py data_engine/configs/scene_default.yaml # agent session that adds diversity +python data_engine/scripts/generate.py --headless --scene scene_1 --num_envs 8 # generate + verify a batch +python data_engine/scripts/render.py --headless --batches # render episodes to video +.venv-lerobot/bin/python vla/convert/convert.py --repo-id # bake a LeRobot dataset +``` + +Training and closed-loop evaluation of VLA policies live in [`vla/`](vla/README.md). + +## Repository layout + +``` +robobench/ EmbodiedSWE-Bench: the benchmark package + core/ BaseEnv, BaseScene, BaseRobot, controller and grader contracts, registries + suites/ task suites: assembly, packing, puzzle, deformable, cutting, locomanip + robots/ embodiments: franka, xarm7, attached (Kinova Gen3 + panda hand), g1, multi (bimanual), ... + controllers/ joint, diff_ik, task_space (OSC / impedance), pink_ik, composite, loco_policy + scripts/smoke.py build and step any registered env +eval/ dockerized agent evaluation +data_engine/ EmbodiedSWE-Gen: expands one solution into diverse trajectories +vla/ bake episodes into LeRobot datasets, train and evaluate VLA policies +rl/ RL baselines (not agent improvement) +sim_gen/ generates new simulation tasks from seed tasks (agent improvement) +real_to_sim/ real scenes and objects to sim: splat backgrounds, photos to sim-ready assets +scripts/ bootstrap installers, record_video.py, asset vendoring +``` + +## Citation + +If you use EmbodiedSWE in your research, please cite our [paper](https://arxiv.org/abs/2609.27308): + +```bibtex +@misc{embodiedswe2026, + title = {EmbodiedSWE: Coding Agents for Long Horizon Dexterous Robotics}, + author = {Shen, Zeyu and You, Haoxiang and Liu, Yilang and Zheng, Zhicheng and Zha, Lihan and + Yamazaki, Kashu and Zhang, Mingtong and Huang, Suning and Sun, Jiankai and + Chen, Qianzhong and He, Lucy and Liu, Kaiyuan and Chang, Haoran and Fragkiadaki, Katerina and + Shah, Dhruv and Schwager, Mac and Henderson, Peter and Abraham, Ian and Xu, Canwen}, + year = {2026}, + eprint = {2609.27308}, + archivePrefix = {arXiv}, + primaryClass = {cs.RO}, + url = {https://arxiv.org/abs/2609.27308}, +} +``` + +## License + +Apache 2.0. See [LICENSE](LICENSE). + +Built on [Isaac Lab](https://github.com/isaac-sim/IsaacLab), [Newton](https://github.com/newton-physics/newton), +and [LeRobot](https://github.com/huggingface/lerobot). diff --git a/reference/data_engine/engine/replay.py b/reference/data_engine/engine/replay.py new file mode 100644 index 0000000000000000000000000000000000000000..5155ed3aeb9381031eadd11df22aeae5525d05cd --- /dev/null +++ b/reference/data_engine/engine/replay.py @@ -0,0 +1,603 @@ +"""replay — L5 visual: re-render recorded episodes kinematically, in parallel envs. + +Generation records full restorable states per sim tick (`ep_NNNN/traj.npz`, keys = +the flattened `env.get_states()` tree). Replay rebuilds the env from the CELL's +LOCAL scene (same as generation), restores E episodes at once — one per env slot — +through `env.set_states`, and renders every env in a single pass with a TiledCamera. +Physics never decides anything: every rendered frame is a restored recorded state +(plus the one sim tick that syncs PhysX -> renderer), so visual changes (camera, +lighting, textures) apply retroactively to every episode ever recorded. + +The parts that bite (all handled here): + - env-origin shift: recorded states are world-frame on the SOURCE batch's env grid; + each replay slot shifts them onto its own origin, anchored on the robot base + (deterministic, never jittered): shift_e = replay_base_e - recorded robot/root[0]. + - scene.post_step() runs on the restored state BEFORE the render, so state-coupled + visuals (the bulb-glow mechanic) land in the same frame, not one frame late. + - the RTX temporal denoiser accumulates across frames ("visual cache"): a teleport + leaves ghost trails, and the first frames of a boot render dark/noisy. Warmup + renders at every chunk start flush it before any frame is kept. + - Kit renders the state PhysX had at its last step: `sim.step(render=True)` (one + tick from the restored state, velocities as recorded) is what syncs transforms + into the renderer — `sim.render()` alone would show the previous frame's poses. + +Cameras are DECLARED, two kinds with two owners: the scene's `CAMERAS` are external +views (env-origin-relative on the work surface — where to stand to see THIS +geometry), the robot's `CAMERAS` are ego views (a `link` key mounts the camera on +that body, eye/target in the link frame — it rides the link through the replayed +motion). All declared views render simultaneously in one pass, one TiledCamera +each; `--cams` selects a subset, and `--eye/--target` adds a one-time ad-hoc view +(named by `--cam`) for probing before numbers get written into a declaration. +Per-view `bands` randomize the pose with THE sampling grammar (engine/sampler.py) +on {eye,target}_{x,y,z}: nominal = the declared value, drawn PER EPISODE (Halton +index = the episode's global position in the run), each episode holding its own +fixed camera — set before the chunk's warmup, so the denoiser never sees it move. + +Outputs, per (episode, view): `imgs/.mp4` (THE dataset video — one x264 +stream at the true control-rate fps; LeRobot's native storage is mp4, so frames +never exist as files) and `imgs/render_.json` (frame indices + the ACTUAL +per-episode camera + joint names + visual draw — the export contract, per view so +several views/looks coexist). Completion is atomic: the video streams to +`.part.mp4`, renamed on close, and the json is written after — a `.part` +file or a missing json marks a killed run, cleaned by the re-render. Per batch: +`replay_sheet_.png` (episodes x time contact sheet, sampled from the videos). + +Visual diversification is SCENE-OWNED, like world physics: the cell's scene.py +declares `VISUAL_PARAMS` bands (same grammar as `PHYSICAL_PARAMS`, cfg default = +the nominal look). `visual_draw=k` samples the bands at Halton index k into ONE +stage-wide look for the whole render pass (lights and ground are shared prims, so a +pass has a single look; K looks of the same episodes = K render runs, distinct +`--cam` names keep them all). The draw is applied at BOTH moments, so a knob needs +no tag saying which kind it is: sampled values are written onto the scene cfg +before the build (build-consumed fields — a table preset, a backdrop usd — take +effect there with no extra code), then handed to `scene.apply_visual_params` after +it (the LIVE subset: attribute writes, material rebinds; fields with no branch +there already did their work at build). The draw index and values land in +render_.json. The `--visual file.py` hook stays as the escape hatch for looks +that don't band-ify (`setup(env)` once after the build, `per_frame(env, t)` before +each rendered frame); this module stays generic. + +Needs a running AppLauncher (see scripts/render.py). One scene per process — the +CLI splits multi-scene inputs into one subprocess per scene group. +""" + +from __future__ import annotations + +import json +import math +import os +from datetime import datetime, timezone +from pathlib import Path + +from .generation import _load, build_env + +WARMUP_DEFAULT = 12 # throwaway renders per chunk start (temporal-denoiser flush) +_FAR_CLIP = 40.0 # camera far clip (m); env_spacing beyond it isolates envs pixel-exactly +# Mean RGB (0-255) a warmed-up first frame must reach; a stage lit by the wrong dome light +# sits at ~15-30 while a correctly lit pc_ram view sits at ~110-130 (2026-08-26 probes). +MIN_WARMUP_LUMA = 35.0 + + +# ----- episode discovery ------------------------------------------------------------------------- +def batch_scene(batch_dir: Path) -> str: + """The scene cell a batch was generated on (from its meta's `cell`).""" + return json.loads((batch_dir / "meta.json").read_text())["cell"].split("/")[0] + + +def collect_episodes(gen_root: Path, batches: list[str] | None = None, + episodes: list[str] | None = None) -> list[Path]: + """Episode dirs to replay: explicit `episodes`, else every ep of `batches` + (names under data/), else every ep of every batch.""" + if episodes: + out = [Path(e) for e in episodes] + missing = [p for p in out if not (p / "traj.npz").is_file()] + if missing: + raise SystemExit(f"not episode dirs (no traj.npz): {missing}") + return out + data = gen_root / "data" + dirs = [data / b for b in batches] if batches else sorted(d for d in data.iterdir() if d.is_dir()) + return [ep for d in dirs for ep in sorted(d.glob("ep_*")) if (ep / "traj.npz").is_file()] + + +def group_by_scene(eps: list[Path]) -> dict[str, list[Path]]: + """{scene: [ep dirs]} — replay builds one env per scene, so inputs split here.""" + groups: dict[str, list[Path]] = {} + for ep in eps: + groups.setdefault(batch_scene(ep.parent), []).append(ep) + return groups + + +# ----- state plumbing ---------------------------------------------------------------------------- +def _unflatten(flat: dict) -> dict: + """{"scene/bulbs": t, "robot/controller/0/prev_action": t} -> the nested set_states tree.""" + out: dict = {} + for key, v in flat.items(): + node = out + *parents, leaf = key.split("/") + for p in parents: + node = node.setdefault(p, {}) + node[leaf] = v + return out + + +def _lookat_quat(eye, target): + """World-convention (+X fwd, +Z up) look-at orientation as (w, x, y, z).""" + import torch + + from isaaclab.utils.math import quat_from_matrix + + f = torch.tensor([t - e for t, e in zip(target, eye)], dtype=torch.float32) + f = f / f.norm().clamp_min(1e-9) + up = torch.tensor([0.0, 0.0, 1.0]) + left = torch.linalg.cross(up, f) + if left.norm() < 1e-6: # looking straight up/down: pick an arbitrary left + left = torch.tensor([0.0, 1.0, 0.0]) + left = left / left.norm() + u = torch.linalg.cross(f, left) + return tuple(quat_from_matrix(torch.stack([f, left, u], dim=1)).tolist()) + + +# ----- cameras: declared views + the ad-hoc override ---------------------------------------------- +_CAM_BAND_KEYS = ("eye_x", "eye_y", "eye_z", "target_x", "target_y", "target_z") + + +def resolve_views(scene_cls, robot_cls, cams: list[str] | None, adhoc: dict | None) -> dict[str, dict]: + """The views to render: the SCENE's `CAMERAS` (external, env-origin-relative on the work + surface) merged with the ROBOT's `CAMERAS` (ego — a `link` key mounts the camera on that + body, eye/target in the link frame), plus the CLI's one-time ad-hoc view. `cams` selects + by name (None = all declared + the ad-hoc). Per-view `bands` use THE sampling grammar + (engine/sampler.py) on {eye,target}_{x,y,z} — nominal = the declared value, drawn per + episode; ego views can't band (their pose is the link's).""" + from .sampler import _check_spec + + views = {n: {**spec, "link": None} for n, spec in getattr(scene_cls, "CAMERAS", {}).items()} + for n, spec in getattr(robot_cls, "CAMERAS", {}).items(): + if n in views: + raise SystemExit(f"camera '{n}' declared by both {scene_cls.__name__} and {robot_cls.__name__}") + views[n] = {**spec, "robot_prim": robot_cls().prim_name} + if adhoc: + views[adhoc["name"]] = {**adhoc, "link": None} + for n, v in views.items(): + bands = v.get("bands") or {} + if bands and v.get("link"): + raise SystemExit(f"camera '{n}': ego views (link-mounted) can't declare bands") + unknown = set(bands) - set(_CAM_BAND_KEYS) + if unknown: + raise SystemExit(f"camera '{n}': bands on {sorted(unknown)} (allowed: {_CAM_BAND_KEYS})") + v["bands"] = {k: _check_spec(f"CAMERAS['{n}']", k, s) for k, s in bands.items()} + v.setdefault("focal", 16.0) + if cams: + missing = sorted(set(cams) - set(views)) + if missing: + raise SystemExit(f"unknown cameras {missing}; declared: {sorted(views)}") + views = {n: views[n] for n in cams} + if not views: + raise SystemExit("no cameras: the scene/robot declare none — pass --eye/--target for a one-time view") + return views + + +def _camera_cfg(name: str, view: dict, size, surface_z: float): + """Per-env TiledCamera for one view. External: under {ENV_REGEX_NS}/, eye/target + env-origin-relative on the work surface (surface_z added here). Ego: under the robot + link ({ENV_REGEX_NS}///), eye/target in the LINK frame — the camera + rides the link through the replayed motion. `focal` is USD mm on the default 20.955 + aperture (24 ≈ 47° hFOV, 16 ≈ 66°).""" + import isaaclab.sim as sim_utils + from isaaclab.sensors import TiledCameraCfg + + eye, target = view["eye"], view["target"] + if view.get("link"): + prim = "{ENV_REGEX_NS}/%s/%s/%s" % (view["robot_prim"], view["link"], name) + else: + prim = "{ENV_REGEX_NS}/" + name + eye = (eye[0], eye[1], eye[2] + surface_z) + target = (target[0], target[1], target[2] + surface_z) + return TiledCameraCfg( + prim_path=prim, + width=size[0], height=size[1], + data_types=["rgb"], + update_period=0.0, + offset=TiledCameraCfg.OffsetCfg(pos=tuple(eye), rot=_lookat_quat(eye, target), convention="world"), + spawn=sim_utils.PinholeCameraCfg(focal_length=view["focal"], clipping_range=(0.05, _FAR_CLIP)), + ) + + +def build_replay_env(scene_dir: Path, num_envs: int, device: str, + cams: list[str] | None, adhoc: dict | None, size, + env_spacing: float, visual_draw: int | None = None, + view_suffix: str = "", + pose_jitter: tuple[float, ...] | None = None): + """generation.build_env on the cell's LOCAL scene (nominal world — physics is + overwritten every frame anyway), with one tiled camera PER RESOLVED VIEW injected + into the scene's assets before the build. + + RTX renders ONE shared stage (no per-env world isolation like Madrona/ManiSkill), + so standalone-robot frames come from geometry: `env_spacing` spreads the replay + grid wider than the camera's far clip (`_FAR_CLIP`) — neighbors are clipped before + rasterization, which is pixel-identical to independent worlds. The recorded states + shift onto whatever grid replay builds, so the spacing is free. The shared ground + plane is enlarged to keep a floor under the far-flung envs.""" + import math + import re + + from robobench.core.registries import SCENES + + _load("datagen_local_scene", scene_dir / "scene" / "scene.py") + scene_name = re.search(r'@SCENES\.register\("([\w.]+)"\)', + (scene_dir / "scene" / "scene.py").read_text()).group(1) + import robobench + import yaml + + robobench.discover() + from robobench.core.registries import ENVS, ROBOTS + + scene_cls = SCENES.get(scene_name) + gen = yaml.safe_load((scene_dir.parents[1] / "gen.yaml").read_text()) + preset_cfg = ENVS.get(gen["preset"])() + # the PRESET's scene cfg — the one the world is built with (generation._local_scene_cfg) + # — not the scene class default: the coffee franka preset puts the counter at 0.55 while + # the class default is 0.994, so every declared camera rendered 0.444 m too high + from .generation import _local_scene_cfg + surface_z = float(getattr(_local_scene_cfg(scene_cls, preset_cfg.scene_cfg, None), + "surface_z", 0.0) or 0.0) + robot_cls = ROBOTS.get(preset_cfg.robot) + views = resolve_views(scene_cls, robot_cls, cams, adhoc) + if pose_jitter and any(pose_jitter): + # Per-episode pose jitter for DRAW passes, expressed through the native + # band grammar (sampled per episode in place_banded_views): each external + # view gets uniform ± bands around its declared pose. Scene-declared + # bands win — the scene's own knowledge is never overridden. + from .sampler import _check_spec + + ej, tj = pose_jitter[:3], pose_jitter[3:] + for n, v in views.items(): + if v.get("link"): + continue # ego views ride their link; there is no free pose + bands = dict(v["bands"]) + for prefix, base, box in (("eye", v["eye"], ej), ("target", v["target"], tj)): + for a, b, w in zip("xyz", base, box): + key = f"{prefix}_{a}" + if w and key not in bands: + bands[key] = _check_spec( + f"pose_jitter['{n}']", key, + {"dist": "uniform", "lo": float(b) - w, "hi": float(b) + w}) + v["bands"] = bands + if view_suffix: + views = {n + view_suffix: v for n, v in views.items()} + # the visual draw is sampled BEFORE the build and written onto the scene cfg, so + # build-consumed knobs (a table preset, a backdrop usd) take effect with no extra + # code; live knobs are re-applied through scene.apply_visual_params after the build + visual_values: dict = {} + if visual_draw is not None: + from .sampler import sample, scene_bands + + bands = scene_bands(scene_cls, scene_cls().cfg, attr="VISUAL_PARAMS") + if not bands: + raise SystemExit(f"--visual_draw: {scene_cls.__name__} declares no VISUAL_PARAMS bands") + visual_values = sample(bands, visual_draw) + cam_cfgs = {n: _camera_cfg(n, v, size, surface_z) for n, v in views.items()} + # entities spawn in insertion order (scene assets, then robot assets): ego cameras + # need the robot LINK prim to exist, so they inject into the ROBOT's assets — after + # the Robot entry — while external views ride the scene's + static_cfgs = {n: c for n, c in cam_cfgs.items() if not views[n].get("link")} + ego_cfgs = {n: c for n, c in cam_cfgs.items() if views[n].get("link")} + # grid extent + far-clip margin on every side + ground_xy = (math.ceil(math.sqrt(num_envs)) - 1) * env_spacing + 2 * (_FAR_CLIP + 10.0) + + def assets_with_camera(self, _orig=scene_cls.assets): + from isaaclab.sim import GroundPlaneCfg + + out = {**_orig(self), **static_cfgs} + for asset in out.values(): + spawn = getattr(asset, "spawn", None) + if isinstance(spawn, GroundPlaneCfg) and max(spawn.size) < ground_xy: + spawn.size = (ground_xy, ground_xy) + return out + + def assets_with_ego(self, _orig=robot_cls.assets): + return {**_orig(self), **ego_cfgs} + + scene_cls.assets = assets_with_camera + robot_cls.assets = assets_with_ego + try: + env, gen, _, _ = build_env(scene_dir, num_envs, device, seed=0, nominal=True, + env_spacing=env_spacing, + scene_overrides=visual_values or None) + finally: + scene_cls.assets = assets_with_camera.__defaults__[0] + robot_cls.assets = assets_with_ego.__defaults__[0] + quenched = _quench_asset_dome_lights() + if quenched: + print(f"[replay] deactivated {len(quenched)} asset-embedded dome light(s) so the scene's " + f"own light is the one RTX uses: {quenched[:3]}{' …' if len(quenched) > 3 else ''}", + flush=True) + return env, gen, visual_values, views, surface_z + + +def _quench_asset_dome_lights() -> list[str]: + """RTX honors ONE dome light per stage and picks it at process start — not deterministically. + Asset USDs can smuggle their own (GAMING_PC.usdc ships a 1.0-intensity DomeLight under the + case's visual), so a render process had a coin-flip chance of lighting the whole scene with + that instead of the scene's /World light: every episode in the process came out "lights off" + (pc_ram stage-1 sweep, 29/29 dark, 2026-08-26). Deactivate every DomeLight that lives inside + an env's asset tree; scene-level lights (spawned at /World/) are left alone.""" + import omni.usd + + stage = omni.usd.get_context().get_stage() + quenched: list[str] = [] + for prim in list(stage.Traverse()): + path = str(prim.GetPath()) + if prim.GetTypeName() == "DomeLight" and path.startswith("/World/envs/"): + prim.SetActive(False) + quenched.append(path) + return quenched + + +def _replay_anchor(robot): + """(live anchor articulation, recorded root key, joint-names stamp) for single + robots AND MultiRobot composites. Composites have no robot.articulation; their + recorded state trees flatten to robot//... — the anchor is the first + child by name (deterministic), and the stamp maps every child's joints.""" + children = getattr(robot, "robots", None) + if isinstance(children, dict): + name = sorted(children)[0] + return (children[name].articulation, f"robot/{name}/root", + {n: list(c.articulation.joint_names) for n, c in sorted(children.items())}) + return robot.articulation, "robot/root", list(robot.articulation.joint_names) + + +def _load_shifted(ep_dir: Path, base_pos, root_key: str, device): + """Episode arrays as device tensors, positions shifted onto the replay slot's + origin. Every last-dim-13 array is a world-frame root state (the engine's own + convention: pos 0:3, quat 3:7, vels 7:13) — only those get the shift.""" + import numpy as np + import torch + + data = {k: torch.as_tensor(v, device=device) for k, v in np.load(ep_dir / "traj.npz").items()} + shift = base_pos - data[root_key][0, 0:3] + for k, v in data.items(): + if k != "action" and v.shape[-1] == 13: + v[..., 0:3] += shift + meta = json.loads((ep_dir / "meta.json").read_text()) + meta["_ep_dir"] = str(ep_dir) + return data, meta + + +# ----- the replay itself ------------------------------------------------------------------------- +def replay_scene(gen_root: Path, scene: str, eps: list[Path], *, num_envs: int = 8, + fps: int | None = None, size=(640, 480), cams: list[str] | None = None, + adhoc: dict | None = None, warmup: int = WARMUP_DEFAULT, + crf: int = 18, max_frames: int = 0, trim_margin: int = -1, + visual: str | None = None, + visual_draw: int | None = None, env_spacing: float = 50.0, + view_suffix: str = "", + pose_jitter: tuple[float, ...] | None = None, + band_seed: int = 0, + device: str = "cuda:0") -> list[Path]: + """Replay `eps` (all from `scene`) in chunks of `num_envs`, rendering every resolved + view each frame and streaming one `imgs/.mp4` per (episode, view) into each + episode dir. Returns the episode dirs rendered. + + Traj rows are one per env.step = one per control latch (row_dt = sim_dt x the + recorded decimation, both stamped in each ep's meta.json). fps=None (the default) + renders one frame PER LATCH — fps = the batch's control rate, the matched regime + for VLA export; pass fps to subsample (e.g. --fps 30 on a 240 Hz batch).""" + import imageio + import numpy as np + import torch + + scene_dir = gen_root / "scenes" / scene + # Never build more envs than episodes: idle slots are padded duplicates that + # still get stepped and tile-rendered every frame (a 2-episode shard at + # num_envs=16 cost ~8x on Newton cloth). + num_envs = max(1, min(num_envs, len(eps))) + if num_envs > 1 and env_spacing <= _FAR_CLIP: + print(f"[replay] WARNING: env_spacing {env_spacing} <= far clip {_FAR_CLIP} — " + f"neighbor envs will appear in frames", flush=True) + env, gen, visual_values, views, surface_z = build_replay_env( + scene_dir, num_envs, device, cams, adhoc, size, env_spacing, visual_draw, + view_suffix=view_suffix, pose_jitter=pose_jitter) + sensors = {n: env.iscene.sensors[n] for n in views} + print(f"[replay {scene}] views: " + ", ".join( + f"{n} (ego on {v['link']})" if v.get("link") else n for n, v in views.items()), flush=True) + if visual_values: + # the draw already sat on the scene cfg through the build (build-consumed knobs + # took effect there); the hook now applies the LIVE subset — attribute writes, + # material rebinds — before the escape-hatch hook and before any warmup + env.scene.apply_visual_params(env, visual_values) + print(f"[replay {scene}] visual draw {visual_draw}: " + + ", ".join(f"{k}={v:.4g}" if isinstance(v, float) else f"{k}={v}" + for k, v in sorted(visual_values.items())), flush=True) + hook = _load("datagen_visual_hook", Path(visual)) if visual else None + if hook and hasattr(hook, "setup"): + hook.setup(env) + env.reset(seed=0) + anchor, root_key, joint_names_stamp = _replay_anchor(env.robot) + base_pos = anchor.data.root_pos_w.clone() # (E, 3) — the shift anchor + + def render_once(): + """One restored-state frame: post_step BEFORE the render (glow in-frame), then + the sim tick that carries the transforms into the renderer.""" + env.iscene.update(env.dt) + env.robot.post_step() + env.scene.post_step() + env.sim.step(render=True) + out = {} + for n, cam in sensors.items(): + cam.update(0.0, force_recompute=True) + out[n] = cam.data.output["rgb"] + return out + + env_origins = env.iscene.env_origins # (E, 3), device + + def place_banded_views(lo: int, n_eps: int) -> dict[str, list]: + """Draw each banded view's per-episode pose (THE sampler, index = the episode's + global position in this run) and set every env slot's camera before the chunk's + warmup — each episode holds its own fixed camera for its whole duration. Returns + {view: [(eye, target) per episode]} for the render contract.""" + from .sampler import sample + + placed: dict[str, list] = {} + for n, v in views.items(): + if not v["bands"]: + continue + poses, quats, actual = [], [], [] + for i in range(num_envs): + # padded slots reuse the last episode's draw; band_seed keeps + # different passes' draws distinct AND deterministic + g = lo + min(i, n_eps - 1) + band_seed + draw = sample(v["bands"], g) + e = [draw.get(f"eye_{a}", x) for a, x in zip("xyz", v["eye"])] + t = [draw.get(f"target_{a}", x) for a, x in zip("xyz", v["target"])] + if i < n_eps: + actual.append((e, t)) + e_w = [e[0], e[1], e[2] + surface_z] + poses.append(env_origins[i] + torch.tensor(e_w, device=device)) + quats.append(torch.tensor(_lookat_quat(e, t), device=device)) + sensors[n].set_world_poses(torch.stack(poses), torch.stack(quats), + convention="world") + placed[n] = actual + return placed + + # same-T episodes chunk together (a batch shares T); mixed chunks pad with the last state + eps = sorted(eps, key=lambda p: (json.loads((p / "meta.json").read_text())["steps"], str(p))) + done: list[Path] = [] + for lo in range(0, len(eps), num_envs): + chunk = eps[lo:lo + num_envs] + loaded = [_load_shifted(ep, base_pos[e], root_key, device) for e, ep in enumerate(chunk)] + # controller state is dropped: stateless leaves flatten to nothing (a composite + # can't restore a partial tree), and kinematic replay never applies an action + keys = [k for k in loaded[0][0] + if k not in ("action", "action_noise") and "/controller" not in k] + + def ep_rows(d: dict, m: dict) -> int: + rows = d[root_key].shape[0] + # trim the padded post-success tail: wide batches hold every finished + # env until the slowest one ends, and meta.success_step (earliest + # SUSTAINED success, generation-time graded) marks where this env was + # actually done. margin keeps the settle visible. + if trim_margin >= 0 and m.get("success_step") is not None: + rows = min(rows, int(m["success_step"]) + trim_margin) + return rows + + T = [ep_rows(d, m) for d, m in loaded] + t_max = max(T) + # a traj row = one env.step = one control latch; the recorded control rate sets + # the frame clock, NOT the rebuilt env's physics dt (the solve may have decimated) + row_dts = {m.get("sim_dt", env.dt) * m.get("decimation", 1) for _, m in loaded} + if len(row_dts) > 1: + raise SystemExit(f"[replay {scene}] chunk mixes control rates " + f"({sorted(1 / d for d in row_dts)}) — render the batches separately") + row_dt = row_dts.pop() + stride = max(1, round(1.0 / (fps * row_dt))) if fps else 1 + fps_out = int(round(1.0 / (row_dt * stride))) + n_frames = len(range(0, t_max, stride)) + if max_frames: + n_frames = min(n_frames, max_frames) + print(f"[replay {scene}] chunk {lo // num_envs + 1}: {len(chunk)} eps, " + f"{t_max} rows @ {1 / row_dt:.0f}Hz control -> {n_frames} frames each " + f"@ {fps_out}fps", flush=True) + + # per (episode, view): one dataset-video writer, streamed to `.part.mp4` and + # renamed only on close — a killed run never leaves a file that looks complete + writers, indices = [], [] + for d, meta in loaded: + ep_dir = Path(meta["_ep_dir"]) + img_dir = ep_dir / "imgs" + img_dir.mkdir(exist_ok=True) + ws = {} + for n in views: + ws[n] = imageio.get_writer(str(img_dir / f"{n}.part.mp4"), fps=fps_out, + codec="libx264", quality=None, pixelformat="yuv420p", + output_params=["-crf", str(crf), "-preset", "medium"]) + writers.append(ws) + indices.append([]) + + def compose(t: int) -> dict: + flat = {} + for k in keys: + rows = [d[k][min(t, T[i] - 1)] for i, (d, _) in enumerate(loaded)] + rows += [rows[-1]] * (num_envs - len(rows)) # pad unused slots + flat[k] = torch.stack(rows) + return _unflatten(flat) + + placed = place_banded_views(lo, len(chunk)) # per-episode camera draws, pre-warmup + env.set_states(compose(0)) + for _ in range(max(0, warmup)): + render_once() + # Lighting guard: a stage lit by the wrong/no light renders every frame of every + # episode in this process dark (the pc_ram stage-1 sweep silently wrote 29 dark + # episodes). Fail loudly on the first frame instead of shipping a dark dataset. + probe = render_once() + for n, r in probe.items(): + luma = r[:len(chunk), ..., :3].float().mean().item() + if luma < MIN_WARMUP_LUMA: + raise SystemExit( + f"[replay {scene}] view {n!r} renders dark after warmup (mean rgb {luma:.1f} " + f"< {MIN_WARMUP_LUMA}) — a light is missing or the wrong dome light won; " + f"see _quench_asset_dome_lights. Refusing to render this chunk.") + + for fi, t in enumerate(range(0, t_max, stride)): + if max_frames and fi >= max_frames: + break + env.set_states(compose(t)) + if hook and hasattr(hook, "per_frame"): + hook.per_frame(env, t) + rgbs = {n: r.cpu().numpy() for n, r in render_once().items()} # each (E, H, W, 3) uint8 + for i in range(len(chunk)): + if t >= T[i]: # this episode already ended — freeze, don't record + continue + for n in views: + writers[i][n].append_data(rgbs[n][i]) + indices[i].append(t) + + for i, (d, meta) in enumerate(loaded): + ep_dir = Path(meta["_ep_dir"]) + for n, v in views.items(): + writers[i][n].close() + os.replace(ep_dir / "imgs" / f"{n}.part.mp4", ep_dir / "imgs" / f"{n}.mp4") + eye, target = (placed[n][i] if n in placed else (v["eye"], v["target"])) + (ep_dir / "imgs" / f"render_{n}.json").write_text(json.dumps({ + "camera": n, "video": f"{n}.mp4", "video_crf": crf, + "size": list(size), "fps": fps_out, "stride": stride, + "sim_dt": env.dt, "step_dt": row_dt, "frame_indices": indices[i], + "eye": list(eye), "target": list(target), "focal": v["focal"], + "link": v.get("link"), "cam_draw": (lo + i if n in placed else None), + "env_spacing": env_spacing, + "joint_names": joint_names_stamp, + "scene_description": env.scene.describe(), + "success": meta.get("success"), "cell": meta.get("cell"), + "visual_hook": visual, "visual_draw": visual_draw, "visual_values": visual_values, + "created": datetime.now(timezone.utc).isoformat(timespec="seconds"), + }, indent=2) + "\n") + done.append(ep_dir) + del loaded + torch.cuda.empty_cache() if device.startswith("cuda") else None + return done, list(views) + + +def contact_sheet(batch_dir: Path, view: str, cols: int = 5) -> Path | None: + """episodes x time grid sampled from the rendered videos -> /replay_sheet_.png.""" + import imageio + import numpy as np + + rows = [] + for ep in sorted(batch_dir.glob("ep_*")): + video = ep / "imgs" / f"{view}.mp4" + rjson = ep / "imgs" / f"render_{view}.json" + if not (video.is_file() and rjson.is_file()): + continue + n = len(json.loads(rjson.read_text())["frame_indices"]) + if n == 0: + continue + picks = [min(int(i * (n - 1) / (cols - 1)), n - 1) for i in range(cols)] + reader = imageio.get_reader(str(video)) + try: + rows.append(np.concatenate([reader.get_data(p) for p in picks], axis=1)) + finally: + reader.close() + if not rows: + return None + sheet = batch_dir / f"replay_sheet_{view}.png" + imageio.imwrite(str(sheet), np.concatenate(rows, axis=0)) + return sheet diff --git a/reference/robobench/README.md b/reference/robobench/README.md new file mode 100644 index 0000000000000000000000000000000000000000..68145a846444648cadf260d14c138e242bbb2bad --- /dev/null +++ b/reference/robobench/README.md @@ -0,0 +1,260 @@ +# RoboBench + +A benchmark for **code-generating agents**. It tests whether an agent can *few-shot* solve +**long-horizon, complex tasks** — multi-step problems where the agent has to write code that plans, +manipulates, and recovers across a long sequence of actions, not just emit a single move. + +RoboBench is organized as a growing family of **suites**, each a different flavor of hard task +(robot furniture assembly is the first; other flavors — e.g. liquid handling / latte-art benches — +can slot in the same way). The simulator backend is deliberately *not* baked into the design: +suites compose the same small set of building blocks regardless of what runs underneath, so new +backends and new task families are additive, not rewrites. + +This README is the living statement of the **building blocks and the design philosophy**. + +--- + +## Design philosophy + +**One equation.** This is the heart of the design, not a diagram bolted on after: + +``` +env = scene + robot (+ an optional, hidden verifier) +``` + +That's the entire composition. Everything else is detail under those three nouns. + +- **`BaseScene`** — the object world (the things to manipulate). Embodiment-agnostic; knows nothing + about the robot. **It also carries the goal** (see "No task layer"). +- **`BaseRobot`** — the actor: the embodiment; its actions flow through a **pluggable controller** + chosen by its `control_mode` (see below). +- **`BaseEnv`** — the open shell that holds the simulation and wires a scene and a robot together. + Composition is by **injection**: build a scene and a robot, hand them to the env. Swapping + embodiment = a new env with a different robot. +- **`BaseVerifier`** — a privileged, *optional*, *hidden* scorer. Not part of the env's contract + with the agent; the env runs fine without one. + +All four are import-light — they import without spinning up a simulator, so you can list registries, +read contracts, and run tests cheaply. Only *building* a `BaseEnv` touches the backend (imported +lazily). + +**Registration is import-time, and cheap — because heavy work is deferred.** Scenes, robots, +controllers, and env-configs register themselves into name→factory registries *at import time* (a +`@register` decorator or a `register_env(...)` call runs when the module is imported). So +`robobench.discover()` — which imports every robot, controller, and suite to populate the registries — +is all it takes to make everything resolvable by name. That stays **~milliseconds and never launches +the simulator** only because each module keeps its **top level import-light**: the expensive work +(importing the backend, loading USD, building the scene/robot/sim) is **deferred into methods** +(`assets()`, `build()`, `to_isaaclab()`), never run at module scope. The discipline this demands: +when you add a scene/robot/controller, **keep heavy imports inside methods, not at module top level** — +otherwise discovery, and every "just list what's available", slows to a simulator boot. + +**The env is open, not a sealed box.** The agent reaches the simulation, the scene, the robot, the +raw asset handles, and the underlying state. It may patch an instance, subclass-and-adopt, or +rebuild. Enforcement of "don't cheat" is by *prompt*, not by access control — ablation dials that +restrict access live in the harness (`agent/`), never in the env. + +**No task layer — a scene *is* a task.** There is deliberately no `Task` class / `TASKS` registry. +Each scene is built for one task: it carries its own goal as natural language in `scene.describe()`, +and its success criteria live in an optional `BaseVerifier`. A task class would just re-wrap the +scene's goal and a hidden reward — so it's absorbed into the scene + verifier. (One scene → one +task; a variant task is a new scene.) + +**The verifier is optional, hidden, and outside the agent's loop.** While solving, the agent reads +the open state and designs and trusts *its own* success metric — the env hands it no reward, so the +task is "solve the goal," not "maximize this number." The `BaseVerifier` is a separate, privileged +scorer that exists for everything *around* the solving loop: a human (or an automated harness) +running **large-scale, apples-to-apples evaluation** across agents, and producing a **reward signal +for post-hoc RL / fine-tuning** of an agent. Keeping it optional and hidden lets the *same* scene +serve both open-ended exploration and rigorous, comparable scoring — and a scene can ship with none +at all (pure sandbox). + +**Control is a separate, shared, pluggable layer — selected by the robot.** A `BaseRobot` declares +the **control modes** it supports (`control_modes`, e.g. `("joint", "ee_pose", "osc_impedance")`) +and which is active (`control_mode`); the active mode maps the action vector to joint targets, so +`action_dim` follows the mode. But the *controller itself* is a **shared, reusable component** — its +own module, not baked into each robot: one implementation (joint, end-effector IK, +operational-space / impedance, a whole-body solver, or a **frozen learned policy**) serves many +embodiments. A robot just declares its modes and wires each to a controller with its own joints / +end-effector / gains. This keeps "swap the controller on the same robot + task" a clean, first-class +operation. The env dispatches through `self.robot` every step (late binding), so a patched/swapped +controller — or a custom one via an overridden `apply_action` — takes effect immediately. + +**Distinguish a controller from a skill, and the env from the agent.** A **controller** is the +per-step action→joint map (the control mode). A **skill** is a higher-level primitive the agent +*invokes* ("grasp this", "walk to here") that runs many steps *through* a controller — skills live +in the **agent layer**, not the env. The guiding rule across the whole framework: the env provides +what is **shared and part of the stable contract** (scenes, robots, controllers, the verifier); the +agent owns what **is the research** (its policies, training loops, skill orchestration, curriculum). +Provided layers are baselines + seams, never locked — an agent may use a controller, extend it, or +replace it. + +This is the **System 1 / System 2** split now standard in robot foundation models — a fast, reactive +low-level policy beneath a slow, deliberative high-level one (Figure's **Helix**: a 200 Hz reactive +S1 under a 7–9 Hz VLM S2; NVIDIA **GR00T N1**: a high-rate diffusion-policy S1 under a VLM S2, +explicitly "inspired by human cognition") — i.e. the **cerebellum** (fast motor coordination) under +the **cerebrum** (deliberation/planning). robobench splits along the same line: the **controller is +System 1 / cerebellum** — env-owned, fast, per-step — and the **skill + agent are System 2 / +cerebrum** — agent-owned, slow, deliberative. The env provides the fast substrate; the agent owns +the slow brain. + +> **Controllers — roadmap (coming, not built yet).** The control layer is grown incrementally. +> First a basic set (joint, then an **IK solver**); then, as needed: operational-space / impedance, +> a whole-body solver, **composite** controllers (different controllers over different DOF groups — +> e.g. arms by IK + legs by a locomotion policy), a **residual-RL** wrapper (a learned correction on +> top of a base controller), and **frozen / pure-RL policy** controllers. Crucially, *training* those +> policies (residual or pure locomotion RL) is the **agent's** job — done through the env's reward + +> rollout + snapshot seams; the env only provides the controller slot they plug into. + +**The base classes are strict contracts.** Everything a child must provide is an `@abstractmethod`, +so a subclass that forgets one *cannot be instantiated*. `bind()` is the one concrete shared method +(caches the env back-ref; children extend via `super().bind(env)`). + +**Curriculum is for the agent to drive.** The abstractions exist so an agent can cheaply *change +the problem to debug it* — relax physics, disable collisions, or swap the embodiment to make a hard +step tractable, then tighten back. And because `get_states` / `set_states` can *set* the env to any +state, the agent can **jump straight into a not-yet-learned state** to debug that phase directly — +instead of replaying the whole task from the start to reach it. Making those switches easy to +explore is a first-class goal. + +**Skills transfer across embodiments and across tasks.** Because the scene is embodiment-agnostic, +the *same* task can run with a *different* body — so "same scene, different robot" (and "same robot, +new scene") is a cheap swap, not a rewrite. This is deliberate: an agent should be able to carry a +skill from one embodiment to another, or from one task to a related one, and few-shot the new case +by adapting what it already knows instead of relearning from scratch — and even to *characterize* +what actually differs between two bodies on a simple task. + +**Batched and RL-ready — so the agent can build its own debug loop.** The env runs many copies in +parallel and exposes full state capture/restore alongside a plain step loop. When an agent gets +stuck on one phase, it can stand up a *small RL environment on the spot* — treat the (otherwise +hidden) success check as a reward, restart from a saved snapshot of the hard phase, and drill just +that segment — without leaving the framework or rewriting the env. Writing a controller, scripting a +plan, or training a small policy to get past a phase are all just things you can do against the same +open object. + +--- + +## Contracts at a glance + +| Class | Children must implement (`@abstractmethod`) | Concrete on the base | +|---|---|---| +| `BaseScene` | `assets · reset · get_state · set_state · describe` | `bind` | +| `BaseRobot` | `assets · reset · describe` (+ hooks `on_bind` / `build_controller`) | `bind · apply_action · action_dim · get_state · set_state · set_controller · actuator_sink · actuator_limits`; declares `control_modes` / `control_mode` | +| `BaseController` | `action_dim · compute · _resolve_joints` | `bind · apply · reset · get_state · set_state`; `command_type` set at construction | +| `BaseVerifier` | `verify` → `Any` *(structured result TBD)* | — | +| `BaseEnv` | *(not abstract — the shell)* | `get_states · set_states · reset · step · describe · describe_stage · verify · close` | + +- **`describe()`** → curated **natural-language** string (scene's objects + goal, then the robot). + Prompt-ready. +- **`describe_stage(root=None, raw=False)`** → the low-level counterpart: structured + `list[{path, type, pos, size}]`, or `raw=True` for the raw scene-graph text. +- **`get_states()` / `set_states()`** → full state = `{"scene": ..., "robot": ...}`; restore to + *any* saved state (snapshots, replay of hard phases). +- **`step()` / `reset()`** return `None` — observations are a wrapper concern, not the raw env's. + +--- + +## Using it — recipes + +> Examples assume `AppLauncher` is running and the registries are populated +> (`import robobench; robobench.discover()`), plus an `env` built as in the first recipe. The env is an +> **open object**: patch or swap parts on the live instance — the step loop dispatches through +> `self.robot` / `self.scene`, so changes take effect on the next `step()`. + +**Build & run an env.** Load a registered binding by name, or wire an ad-hoc combo, then step it: + +```python +import robobench; robobench.discover() # populate registries: robots, controllers, suites +from robobench.core import ENVS, EnvCfg + +env = ENVS.get("assembly.ikea_table")().build(num_envs=4) # a registered config, by name +# ...or ad-hoc: EnvCfg(scene="ikea_table", robot="g1", control_mode="joint").build() +env.reset() +print(env.describe()) # NL scene + goal + robot (prompt-ready) +for _ in range(200): + env.step(action) # action: (num_envs, env.robot.action_dim) +``` + +(Or smoke any combo from the shell: `python -m robobench.scripts.smoke --list` / `--env `.) + +**Switch the control mode.** Same robot + scene, different actuation — `action_dim` follows: + +```python +EnvCfg(scene="ikea_table", robot="g1", control_mode="pink_ik").build() # vs "joint" +# or override at build time: cfg.build(control_mode="pink_ik") +``` + +**Write your own controller and plug it in.** A controller maps an action to a joint command for the +DOFs it owns and writes it itself. Subclass `BaseController`, then drop it onto the live robot with +`set_controller` — it binds and takes effect next `step()`, and need **not** be in the robot's +`control_modes` menu: + +```python +from robobench.core import BaseController + +class ElbowWiggle(BaseController): + def __init__(self, cfg=None): + super().__init__(cfg, command_type="position") # what it writes: position / velocity / effort + def _resolve_joints(self, robot): # which DOFs it drives + return robot.articulation.find_joints([".*_elbow_joint"])[0] + @property + def action_dim(self): return len(self.joint_ids) + def compute(self, action): # PURE: action -> joint targets + return action # self.robot / self.limits available + +env.robot.set_controller(ElbowWiggle()) # live; bypasses control_modes +``` + +`compute` is pure math; the inherited `apply` writes its output through the **sink** the robot handed +the controller at bind (matching `command_type`). For a fully custom path, override `robot.apply_action` +instead. This is the seam for the agent's own controllers — the env owns the slot, the agent owns the policy. + +**Combine controllers.** Two composable axes: + +- *Different joints* → `CompositeController` (the action is split across groups; groups may even use + **different** `command_type`s — e.g. effort arms + position hands, each writing its own): + + ```python + from robobench.controllers import CompositeController, JointController, JointControllerCfg + env.robot.set_controller(CompositeController([ + MyArmController(), # your arm controller + JointController(JointControllerCfg((".*_hand.*",)), command_type="position"), # hands, direct + ])) + ``` + +- *Same joints* → a **residual** wrapper (`base.compute(action) + scale · correction`), itself a + controller, so it nests inside a composite leaf. *(Planned; the correction net is agent-side.)* + +**Tune the build (gains, placement, sim).** It's all config — override on the `EnvCfg`, its +`robot_cfg`, or per-build: + +```python +from robobench.robots import G1RobotCfg +EnvCfg(scene="ikea_table", robot="g1", control_mode="joint", + robot_cfg=G1RobotCfg(arm_stiffness=1500.0, base_pos=(0.0, -0.6, 0.75)), # softer arms, moved back + sim_overrides={"dt": 1 / 200}).build(num_envs=16) +``` + +**Snapshot & restore (debug / RL).** Capture full state and jump back to it — replay a hard phase, or +stand up a small RL loop from a saved snapshot rather than from the episode start: + +```python +s = env.get_states() # {"scene": ..., "robot": ...} +# ... drive / train ... +env.set_states(s) # restore to ANY saved state +``` + +--- + +## Layout + +``` +robobench/ + core/ shared machinery — BaseScene · BaseRobot · BaseEnv · BaseVerifier, + scene introspection (describe_stage), registries (SCENES · ROBOTS) + robots/ reusable embodiments — where concrete control pipelines / gains / IK live + suites/ the task families (one folder per flavor; see each suite's own docs) +``` + +> A per-suite section (what each flavor contains, how to add one) will be added once the first +> suite is built. diff --git a/reference/robobench/core/grader.py b/reference/robobench/core/grader.py new file mode 100644 index 0000000000000000000000000000000000000000..bbc55cea92fad6c5380c9da586d32ca3d92c6cb2 --- /dev/null +++ b/reference/robobench/core/grader.py @@ -0,0 +1,259 @@ +"""Grader — privileged, hidden scorer, applied by wrapping the graded env. + +A grader is a rubric: weighted stages, each an individual method on the +concrete grader. `GradedEnv` wraps the env handed to the delivered +`solve(env)` and records the rubric after every step, so scoring happens +automatically while the delivery runs; when the harness is done stepping, +`verdict()` reads the final state. Never exposed to the agent. +""" + +from __future__ import annotations + +import time +from abc import ABC, abstractmethod +from typing import TYPE_CHECKING, Callable + +if TYPE_CHECKING: + import torch + + from .env import BaseEnv + + +class BaseGrader(ABC): + """A concrete grader MUST provide: a docstring, `SCENE` (the scene class + it grades — the env is type-checked at construction), `RUBRIC`, one + method per stage, `setup()`, and `check_success()` — anything missing or + mismatched fails loudly at construction. + + `RUBRIC` = ((stage, weight) | (stage, weight, "once" | "final"), ...). + Each stage names a method on the grader returning its instantaneous + PER-ENV value in [0, 1] — a `(num_envs,)` tensor (a scalar broadcasts to + every env); discrete 0/1 for a satisfied-or-not check, continuous for a + fraction. A plain (stage, weight) entry is scored LIVE — its current + value, so the curve dips when the state is lost; a "once" entry is a + milestone — scored on the best value ever achieved per env (e.g. a pick + that ends when the part is placed). A "final" entry is a verdict-time + gate (e.g. a rendered frame judged by a VLM): it is NOT evaluated per + step — its value is 0 throughout the run, so the peak progress a run can + record is `1 - w_final / total` — and `verdict()` evaluates it exactly + once (cached on the instance) on the final state. A final-stage method + may return either a value or `(value, details)`, where `details` is a + dict shared by every env or a list of one dict per env; the details land + under `"final"` in the per-trajectory verdict so the judge's inputs and + raw answer are auditable. With final stages, `score` is the larger of + the run's peak progress and the final-state progress with the final + stages filled in, and `success` additionally requires every final stage + to be at 1 — a final stage can only take credit away, never add it. + `progress()` = the weighted sum normalized by the weight total, so + weights are relative and need not sum to 1. The grader is PER-TRAJECTORY + throughout: every record and verdict is reported per env, in the same + format regardless of `num_envs` — aggregation (statistics over the + batch) is the harness scripts' job, never the class's. + """ + + SCENE: type | None = None + RUBRIC: tuple[tuple, ...] = () + + def __init__(self, env: BaseEnv) -> None: + if self.SCENE is None: + raise TypeError(f"{type(self).__name__} declares no SCENE") + if not isinstance(env.scene, self.SCENE): + raise TypeError(f"{type(self).__name__} grades {self.SCENE.__name__}, " + f"got {type(env.scene).__name__}") + if not self.RUBRIC: + raise TypeError(f"{type(self).__name__} declares no RUBRIC") + self._stages = [(e[0], e[1], e[2] if len(e) > 2 else "live") for e in self.RUBRIC] + bad = [n for n, _, mode in self._stages if mode not in ("live", "once", "final")] + if bad: + raise TypeError(f"{type(self).__name__} has unknown stage modes on: {bad}") + missing = [n for n, _, _ in self._stages if not callable(getattr(self, n, None))] + if missing: + raise TypeError(f"{type(self).__name__} is missing stage methods: {missing}") + if any(w <= 0 for _, w, _ in self._stages): + raise TypeError(f"{type(self).__name__} has non-positive rubric weights") + import torch + + self.num_envs = env.num_envs + self._best = {n: torch.zeros(self.num_envs) + for n, _, mode in self._stages if mode == "once"} + self._final_stages = [n for n, _, mode in self._stages if mode == "final"] + self._final_cache: dict | None = None # {stage: (value tensor, [details per env])} + self.env = env + self.scene = env.scene # children re-annotate with their SCENE type + self.steps = 0 + self.history: list[list[dict]] = [] # per step: one record per trajectory (the curves) + self._t0 = time.monotonic() + self.setup() + + @abstractmethod + def setup(self) -> None: + """Capture whatever the rubric needs from the just-reset env (start + poses, references). Runs once at construction.""" + + @property + def sim_time(self) -> float: + return round(self.steps * self.env.dt * self.env.robot.control_period, 4) + + def _per_env(self, value, name: str): + """Coerce a stage/success return to a `(num_envs,)` CPU tensor — + scalars broadcast, anything else must match the env count.""" + import torch + + v = torch.as_tensor(value).detach().reshape(-1).cpu() + if v.numel() == 1 and self.num_envs > 1: + v = v.expand(self.num_envs).clone() + if v.numel() != self.num_envs: + raise ValueError(f"{type(self).__name__}.{name} returned {v.numel()} " + f"values for {self.num_envs} envs") + return v + + def measure(self) -> dict: + """Every stage's scored per-env value (`(num_envs,)` CPU tensor): the + method's clamped return for a live stage, the best ever achieved per + env for a "once" milestone.""" + import torch + + values = {} + for name, _, mode in self._stages: + if mode == "final": # verdict-time only: worth 0 while the run is stepping + values[name] = torch.zeros(self.num_envs) + continue + v = self._per_env(getattr(self, name)(), name).float() + # A NaN stage value (a degenerate sim state, e.g. a restored snapshot with an + # uninitialised body) scores 0: torch.maximum propagates NaN, so without this one NaN + # would poison a "once" milestone for the rest of the run (seen 2026-09-04, box_to_bin + # `carried` when scoring reached-state logs). + v = torch.nan_to_num(v, nan=0.0).clamp(0.0, 1.0) + if mode == "once": + v = self._best[name] = torch.maximum(self._best[name], v) + values[name] = v + return values + + def _evaluate_final(self) -> dict: + """Run every "final" stage exactly once (the result is cached on the + instance — repeated `verdict()` calls reuse it) and return + `{stage: (clamped (num_envs,) value tensor, [details dict per env])}`. + A stage may return a bare value or `(value, details)`; `details` is + either one dict (shared by every env) or a list of `num_envs` dicts.""" + if self._final_cache is not None: + return self._final_cache + out = {} + for name in self._final_stages: + ret = getattr(self, name)() + details = None + if isinstance(ret, tuple) and len(ret) == 2 and isinstance(ret[1], (dict, list)): + ret, details = ret + v = self._per_env(ret, name).float().clamp(0.0, 1.0) + if details is None: + per_env = [{} for _ in range(self.num_envs)] + elif isinstance(details, dict): + per_env = [dict(details) for _ in range(self.num_envs)] + else: + if len(details) != self.num_envs: + raise ValueError(f"{type(self).__name__}.{name} returned {len(details)} " + f"detail dicts for {self.num_envs} envs") + per_env = [dict(d) for d in details] + out[name] = (v, per_env) + self._final_cache = out + return out + + def progress(self, values: dict | None = None): + """The weighted rubric total per env — a `(num_envs,)` tensor.""" + values = self.measure() if values is None else values + total = sum(w for _, w, _ in self._stages) + return sum(w * values[n] for n, w, _ in self._stages) / total + + @abstractmethod + def check_success(self) -> "torch.Tensor | bool": + """Instantaneous, authoritative PER-ENV goal test (e.g. + `scene.seated().all(dim=1)` -> `(num_envs,)` bools; a scalar + broadcasts) — the ladder scores, this decides.""" + + def record(self) -> list[dict]: + """One curve entry PER TRAJECTORY — entry e is env e's ladder + + stage values, in the same format regardless of `num_envs`; + GradedEnv calls this after every step.""" + self.steps += 1 + m = self.measure() + p = self.progress(m) + wall = round(time.monotonic() - self._t0, 3) + recs = [{"sim_time_s": self.sim_time, "wall_s": wall, + "progress": round(float(p[e]), 4), + "stages": {k: round(float(v[e]), 4) for k, v in m.items()}} + for e in range(self.num_envs)] + self.history.append(recs) + return recs + + def verdict(self) -> list[dict]: + """One verdict PER TRAJECTORY — a pure read, no stepping. For env e: + success = the authoritative test on its final state; score = its + highest recorded progress. Statistics over the batch are the + caller's job.""" + succ = self._per_env(self.check_success(), "check_success") + if self.history: + peaks = [max(step[e]["progress"] for step in self.history) + for e in range(self.num_envs)] + else: + peaks = [round(float(x), 4) for x in self.progress()] + if not self._final_stages: + return [{"env": e, "success": bool(succ[e]), "score": peaks[e]} + for e in range(self.num_envs)] + # Final stages: evaluated once on the final state, then folded into the + # ladder. score = max(peak over the run, final-state progress with the + # final stages filled in); success also needs every final stage at 1. + final = self._evaluate_final() + values = self.measure() + for name, (v, _) in final.items(): + values[name] = v + final_progress = self.progress(values) + out = [] + for e in range(self.num_envs): + gate = all(bool(v[e] >= 1.0) for v, _ in final.values()) + out.append({ + "env": e, + "success": bool(succ[e]) and gate, + "score": max(peaks[e], round(float(final_progress[e]), 4)), + "final": {name: {"value": round(float(v[e]), 4), **per_env[e]} + for name, (v, per_env) in final.items()}, + }) + return out + + def describe(self) -> str: + """Criteria provenance — written next to every verdict.""" + doc = (self.__doc__ or "").strip().splitlines()[0] + weights = " · ".join(f"{n} ×{w:g}" + (f" ({mode})" if mode != "live" else "") + for n, w, mode in self._stages) + return f"{type(self).__name__}: {doc} — rubric: {weights}" + + +class GradedEnv: + """What the delivered solve(env) receives instead of the raw env. + + Same surface — every attribute delegates to the wrapped env — except: + step() also records the grader's rubric, and the grading-time shortcuts, + reset and set_states, are blocked. The harness keeps the raw env.""" + + def __init__(self, env: BaseEnv, grader: BaseGrader, + on_record: Callable[[list[dict]], None] | None = None) -> None: + self._env, self._grader, self._on_record = env, grader, on_record + + def __getattr__(self, name): + return getattr(self._env, name) + + def step(self, action, render: bool = False): + out = self._env.step(action, render) + recs = self._grader.record() # one record per trajectory + if self._on_record is not None: + self._on_record(recs) + return out + + def reset(self, *args, **kwargs): + raise PermissionError("blocked during grading — the graded env was reset " + "by the grader; solve may only step forward") + + def set_states(self, *args, **kwargs): + raise PermissionError("blocked during grading — solve may only step forward") + + def verdict(self) -> list[dict]: + """Per-trajectory verdicts — aggregation is the caller's job.""" + return self._grader.verdict() diff --git a/reference/robobench/core/robot.py b/reference/robobench/core/robot.py new file mode 100644 index 0000000000000000000000000000000000000000..7886f23075e1dd3633622fe4033a6c1dd60d021b --- /dev/null +++ b/reference/robobench/core/robot.py @@ -0,0 +1,247 @@ +"""BaseRobot — the actor (embodiment). + +A robot declares its assets + the control modes it supports, and how to read/write its own state. +The per-step action→sim pipeline is handled **generically through a controller**: `bind()` +orchestrates the lifecycle — bind the robot to the env, acquire its sim handles, then build + bind a +controller *to the robot* — and `apply_action` / `action_dim` delegate to that controller. A concrete +robot only fills in **hooks**: `on_bind` (grab handles), `build_controller` (the controller for the +active `control_mode`), and — unless it's a single articulation — `actuator_sink` / `actuator_limits`. +It does not re-implement the lifecycle. Controller-less robots (NullRobot, force-driven debug actors) +just leave `build_controller` returning None. + +The env is open, so an agent can drop in its own controller at runtime with `set_controller(...)` +(or override `apply_action` for a fully custom path). Each controller writes its own command to sim +through the `actuator_sink` it captured at bind, so a composite can mix command types per joint group. + +Heavy imports are deferred so this module imports without AppLauncher. +""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from dataclasses import dataclass, field +from typing import TYPE_CHECKING, Any, ClassVar + +from .config import BaseCfg + +if TYPE_CHECKING: + import torch + + from .controller import BaseController + from .env import BaseEnv + + +@dataclass +class BaseRobotCfg(BaseCfg): + """Thin shared base for robot configs: carries only the one universal selectable, the + `control_mode`. Concrete robots subclass this and add their own asset / partial body variant (e.g. hands) / + `fix_root_link` / ee-frame / gains — morphologies differ too much for a fat shared cfg.""" + + #: Which of the robot's `control_modes` to use; "" -> the first one the robot declares. The agent + #: may switch it (a new actuation of the same hardware). + control_mode: str = "" # active control mode; '' selects the robot's first + + #: The robot's scene namespace: its asset key in `env.iscene` and (capitalized) its prim name — + #: "robot" -> `iscene["robot"]` at `{ENV_REGEX_NS}/Robot`. Single-robot envs keep the default; a + #: composite parent (`MultiRobot`) stamps each child's name ("left", "right", ...) so several + #: robots coexist in one scene without key/prim collisions. Concrete robots read it through + #: `BaseRobot.name` / `BaseRobot.prim_name` rather than hardcoding "robot". + name: str = field(default="robot", kw_only=True) # scene-asset key / prim namespace for this robot + + +class BaseRobot(ABC): + #: Control modes this embodiment supports (e.g. ("joint", "ee_pose", "osc_impedance")). + #: Declared per concrete robot; the active one is `self.control_mode`. + control_modes: tuple[str, ...] = () + + #: Named EGO viewpoints for visual replay (data_engine render.py) — embodiment knowledge: + #: which body is the wrist, where a lens clears the fingers. Each entry: {"link": , "eye": (x,y,z), "target": (x,y,z), "focal": mm}, eye/target in the LINK frame; the + #: camera mounts under that link and rides it. External scene views live on the SCENE's + #: `CAMERAS` instead. + CAMERAS: ClassVar[dict[str, dict]] = {} + + def __init__(self, cfg: Any) -> None: + self.cfg = cfg + self._env: BaseEnv | None = None + self.control_mode: str | None = getattr(cfg, "control_mode", None) or ( + self.control_modes[0] if self.control_modes else None + ) + #: The active controller, built in `bind()` (None for a controller-less robot). The agent may + #: swap it at runtime via `set_controller()`. + self.controller: BaseController | None = None + #: The robot's main articulation handle — set in `on_bind` for the common single-articulation + #: case (the default `actuator_sink` / `actuator_limits` use it). None until bound. + self.articulation: Any = None + + # ----- naming (the robot's scene namespace; see BaseRobotCfg.name) -------------------------- + @property + def name(self) -> str: + """This robot's scene-asset key (`cfg.name`; "robot" for a plain single-robot env). Concrete + robots use it in `assets()` and `on_bind` (`env.iscene[self.name]`) so a composite parent can + re-namespace them just by stamping `cfg.name`.""" + return getattr(self.cfg, "name", None) or "robot" + + @property + def prim_name(self) -> str: + """USD prim name under `{ENV_REGEX_NS}`: `cfg.name` with the first letter upper-cased + ("robot" -> "Robot", "left" -> "Left"), so default single-robot prim paths stay unchanged.""" + n = self.name + return n[:1].upper() + n[1:] + + # ----- assets / state / description (the robot-specific contract) --------------------------- + @abstractmethod + def assets(self) -> dict[str, Any]: + """`{name: cfg}` for the robot prim(s) (ArticulationCfg / RigidObjectCfg / …).""" + + @abstractmethod + def reset(self, env_ids: torch.Tensor) -> None: + """Reset the robot to its home configuration for `env_ids` (also reset `self.controller`).""" + + def get_state(self, env_ids: torch.Tensor) -> dict[str, Any]: + """Full restorable state, uniform across single-articulation robots: the articulation's + sim-resident state (root, joint pos/vel, and the actuator setpoints) plus the controller's own + state (delegated, so it travels with whatever controller is bound). + + To ADD robot-specific state, extend rather than replace — call `super().get_state(env_ids)` for + this uniform part, add your own keys, and restore them after `super().set_state(...)`: + def get_state(self, ids): s = super().get_state(ids); s["tool"] = ...; return s + def set_state(self, s, ids): super().set_state(s, ids); self._tool.write_(s["tool"]) + `set_state` reads only the base keys, so it ignores any extra keys a subclass adds (no clash). + Override fully only if the robot isn't a single articulation (e.g. multi-body).""" + state: dict[str, Any] = {} + art = self.articulation + if art is not None: + d = art.data + state["root"] = d.root_state_w[env_ids].clone() + state["joint_pos"] = d.joint_pos[env_ids].clone() + state["joint_vel"] = d.joint_vel[env_ids].clone() + state["joint_pos_target"] = d.joint_pos_target[env_ids].clone() + state["joint_effort_target"] = d.joint_effort_target[env_ids].clone() + if self.controller is not None: + state["controller"] = self.controller.get_state(env_ids) + return state + + def set_state(self, state: dict[str, Any], env_ids: torch.Tensor) -> None: + """Restore what `get_state` returned: write the articulation's root + joint state and re-apply + the actuator setpoints (so a restored robot resumes its commanded motion), then restore the + controller's state.""" + art = self.articulation + if art is not None: + art.write_root_state_to_sim(state["root"], env_ids) + art.write_joint_state_to_sim(state["joint_pos"], state["joint_vel"], env_ids=env_ids) + art.set_joint_position_target(state["joint_pos_target"], env_ids=env_ids) + art.set_joint_effort_target(state["joint_effort_target"], env_ids=env_ids) + if self.controller is not None and "controller" in state: + self.controller.set_state(state["controller"], env_ids) + + @abstractmethod + def describe(self) -> str: + """**Natural-language** description of the embodiment + its control, for the agent.""" + + # ----- lifecycle: env -> robot -> controller (concrete; override the HOOKS, not bind) ------- + def bind(self, env: BaseEnv) -> None: + """Bind the robot to `env`, then build + bind its controller **to the robot**. The chain: + the env builds the scene, calls this once → it caches the env, `on_bind` grabs sim handles, + `build_controller` makes the controller for the active mode, and `set_controller` binds it to + this robot. Override the hooks (`on_bind` / `build_controller`, or `actuator_sink` / + `actuator_limits` for non-articulation robots), not this.""" + self._env = env + self.on_bind(env) + controller = self.build_controller() + if controller is not None: + self.set_controller(controller) + + def on_bind(self, env: BaseEnv) -> None: + """Hook: grab sim handles once after build (e.g. `self.articulation = env.iscene["robot"]`). + Runs before the controller is built, so the controller can resolve its joints against the + handle. No-op by default.""" + + def build_controller(self) -> BaseController | None: + """Hook: the controller for the active `control_mode` (its `joint_ids` / solver are resolved + when it is bound, in `set_controller`). Return None for a controller-less robot (default).""" + return None + + def set_controller(self, controller: BaseController) -> None: + """Bind `controller` to this robot and install it as the active one. The **supported seam** + for an agent to drop in its own controller at runtime — + `env.robot.set_controller(MyController(...))` — which takes effect on the next `step()` (the + env dispatches through `self.robot`). Bypasses `control_modes`: a custom controller need not + be in the menu.""" + controller.bind(self) + self.controller = controller + + # ----- action + control rate (generic, via the active controller) --------------------------- + @property + def action_dim(self) -> int: + """Width of the action vector = the active controller's (0 if there is none).""" + return self.controller.action_dim if self.controller is not None else 0 + + @property + def control_period(self) -> int: + """Physics substeps the env runs per `env.step` = the active controller's `control_period` + (1 if there is none, i.e. control at sim rate). For a composite this is its slowest leaf.""" + return self.controller.control_period if self.controller is not None else 1 + + def control_dt(self, controller: BaseController) -> float | None: + """Hook: a robot-level control period (s) that OVERRIDES `controller.cfg.dt`; None -> defer to + the controller. Takes the controller so a robot can pin per-leaf rates of a composite. Read at + bind and turned into an integer `control_period`.""" + return None + + def apply_action(self, action: torch.Tensor, substep: int = 0) -> None: + """Run the active controller, which computes **and writes** its command (no-op without one). + `substep` is the physics-step index in the `env.step` window; the controller fires only on its + own subdivision (see `BaseController.apply`). Override for a fully custom action path. + + `action` may be any array-like (numpy / list / torch): controllers assume a float32 torch + tensor on the sim device, so it is coerced here — the one choke point every path shares + (`env.step`, direct calls). Agent-written code passes numpy, which used to surface as a + baffling numpy↔cuda-tensor TypeError deep in the controller's EMA math. A no-op (same + object back) when the input is already a float32 tensor on the sim device.""" + if self.controller is not None: + import torch + + action = torch.as_tensor(action, dtype=torch.float32, device=self.env.device) + if action.dim() == 1: # tolerate a single unbatched action vector + action = action.unsqueeze(0) + self.controller.apply(action, substep) + + def actuator_sink(self, command_type: str): + """The **write path** for a controller's `command_type`: a callable `(command, joint_ids) -> + None` that sends `command` to sim through the matching joint-target setter. A controller + captures this once at bind and writes through it itself — so a `composite` can mix command + types (effort arms + position hands), each leaf writing its own group. Default uses + `self.articulation`; override for multi-body or non-articulation robots.""" + art = self.articulation + setter = { + "position": art.set_joint_position_target, + "velocity": art.set_joint_velocity_target, + "effort": art.set_joint_effort_target, + }[command_type] + return lambda command, joint_ids: setter(command, joint_ids=joint_ids) + + def actuator_limits(self, joint_ids: Any) -> dict[str, Any]: + """Per-joint actuation bounds for `joint_ids`, handed to a controller at bind (`self.limits`) + so it can clamp / scale / validate — most controllers ignore them and stay general. A snapshot + from the articulation (re-query for live values). Keys: ``pos`` (n, k, 2 = lower/upper), + ``vel`` (n, k), ``effort`` (n, k). Override for non-articulation robots.""" + d = self.articulation.data + return { + "pos": d.joint_pos_limits[:, joint_ids, :], + "vel": d.joint_vel_limits[:, joint_ids], + "effort": d.joint_effort_limits[:, joint_ids], + } + + @property + def env(self) -> BaseEnv: + """The env this robot is bound to. Available after `bind()`; raises if accessed before.""" + if self._env is None: + raise RuntimeError("robot is not bound to an env yet (call happens before bind())") + return self._env + + def post_step(self, env_ids: torch.Tensor | None = None) -> None: + """Step-coupled robot bookkeeping, run by the env **once per physics substep** (after the sim + advances), so it tracks every physics step even under control decimation. Default no-op. + Override for things that must track the new state every step (e.g. advancing a controller's + internal target/integrator). Not for the agent to call.""" diff --git a/reference/robobench/robots/franka.py b/reference/robobench/robots/franka.py new file mode 100644 index 0000000000000000000000000000000000000000..32053e38058d6b4a2b8e3a10f23f103adfa00d4f --- /dev/null +++ b/reference/robobench/robots/franka.py @@ -0,0 +1,282 @@ +"""FrankaRobot — a Franka Emika Panda arm + parallel gripper, for table-top manipulation. + +A fixed-base 7-DOF arm with a 2-finger gripper. Five control modes (the gripper is always direct +position targets; switching the mode swaps only the arm controller): + + - "osc" -> arm by operational-space control (`OperationalSpaceController`, inertia-shaped + torque); action = 6 EE pose deltas + 2 gripper = 8. Default — the inertia decoupling + keeps the low-inertia wrist smooth. + - "impedance" -> arm by Jacobian-transpose task-space impedance (`TaskSpaceImpedanceController`); + same 8-D action. The form Isaac's Factory tasks use; on this arm it can shake the + wrist (no inertia decoupling), so it's not the default. + - "diff_ik" -> arm by differential IK (`DiffIKController`, batched DLS step -> joint position + targets tracked by the arm PD); same 8-D action (same delta scales as the torque + modes). The Isaac Lab task-space example recipe (`FRANKA_PANDA_HIGH_PD_CFG`, arm + PD 400/80 — the cfg defaults here). + - "pink_ik" -> arm by Pink multi-task QP IK (`PinkIKController`, per-env CPU solve over the + vendored kinematics URDF) -> joint position targets tracked by the same arm PD; + action = ABSOLUTE hand pose `[pos3, quat4 wxyz]` (env frame) + 2 gripper = 9. + The GR00T-school teleop/retarget solver. NOTE: any script building a pink env + must `import pinocchio` BEFORE AppLauncher (see `controllers/pink_ik.py`). + - "joint" -> arm by direct joint position targets (`JointController`); action = 7 arm + 2 gripper. + +The two torque modes load the arm actuators in TORQUE mode (zero stiffness/damping) so the +controller's torques drive them; "diff_ik"/"pink_ik"/"joint" keep the arm position PD +(`arm_stiffness`/`arm_damping`). So `action_dim` and the arm actuator setup follow `control_mode`. +Heavy imports are deferred so registration stays app-free. +""" + +from __future__ import annotations + +import math +from dataclasses import dataclass +from pathlib import Path +from robobench.core.assets import asset_path +from typing import TYPE_CHECKING, Any, ClassVar + +import torch + +from robobench.controllers import ( + CompositeController, + DiffIKController, + DiffIKControllerCfg, + FrameTaskCfg, + JointController, + JointControllerCfg, + OperationalSpaceController, + PinkIKController, + PinkIKControllerCfg, + TaskSpaceControllerCfg, + TaskSpaceImpedanceController, +) +from robobench.core import ROBOTS, BaseRobot, BaseRobotCfg + +if TYPE_CHECKING: + from isaaclab.assets import Articulation + + from robobench.core import BaseEnv + + +@dataclass +class FrankaRobotCfg(BaseRobotCfg): + """Config for `FrankaRobot`. `control_mode` is inherited from `BaseRobotCfg` ("" -> first declared). + `base_pos`/`base_rot` are placement dials; `fixed_base` + the asset are structural. The arm PD gains + are used only in "joint" mode (OSC zeroes them for torque control); the gripper PD is always on.""" + + fixed_base: bool = True # weld the base to the world (a table-mounted arm) + base_pos: tuple[float, float, float] = (0.0, 0.0, 0.0) # base at the table level + base_rot: tuple[float, float, float, float] = (1.0, 0.0, 0.0, 0.0) # wxyz; faces +x + # Arm position-PD gains — used in the position modes ("joint" / "diff_ik"; the torque modes zero + # them). 400/80 = Isaac's FRANKA_PANDA_HIGH_PD_CFG, "specifically used for IK tracking" — the + # stock 80/4 preset lags task-space targets badly. + arm_stiffness: float = 400.0 + arm_damping: float = 80.0 + # Arm actuator effort cap [N*m]; None -> keep the preset's real-Panda limits (87/12). Raise for + # scripted joint-position tracking that must not crawl at the real limits (kinematic-demo ports). + arm_effort_limit: float | None = None + # Body-level gravity compensation fraction (Newton/MuJoCo backend only; 1.0 = weightless arm). + # The preset's PhysX `disable_gravity` flag is IGNORED by the Newton pipeline — MuJoCo needs + # `gravcomp`, else a kp=400 servo sags ~tau_g/kp (~0.1 rad on the shoulder when extended). + # None -> leave the preset untouched (required under the PhysX/2.x venv). + gravity_compensation: float | None = None + # PhysX-side gravity compensation: disable gravity on the robot's links (the real Panda + # gravity-compensates internally; the task-space torque law here has NO gravity term, so + # with gravity on, extended poses spend the task gains fighting the arm's own weight — + # MEASURED: commanded reorientations at low work poses stall 56-76 deg from target while + # the same rotations track to 1-7 deg from the light tuck pose). + disable_arm_gravity: bool = False + # Gripper PD gains (always position-controlled; holds / grasps the part). + gripper_stiffness: float = 2000.0 + gripper_damping: float = 100.0 + # Gripper actuator effort cap [N]; None -> keep the preset's default. Raise for pinch grips + # that must not saturate (e.g. 500.0 for cloth, the isaaclab soft-lift tasks' value). + gripper_effort_limit: float | None = None + # Home posture of the 7 arm joints: the arm resets here. A forward-facing ready pose; retune per + # task (e.g. to start the gripper near the work). + default_dof_pos: tuple[float, ...] = (0.0015, -0.197, -0.0014, -1.976, -0.00028, 1.78, 0.786) + # Posture the task-space nullspace pulls toward; () -> use default_dof_pos. A non-singular elbow + # config that actively resolves the arm's redundancy (keeps the wrist from drifting); kept separate + # from the home pose on purpose. + nullspace_dof_pos: tuple[float, ...] = (-1.3003, -0.4015, 1.1791, -2.1493, 0.4001, 1.9425, 0.4754) + # Nullspace posture stiffness; None -> the controller's default (10.0). MEASURED: at 10.0 + # the posture spring overpowers the task torque for world-z reorientations (stalls 76 deg + # from target with every joint far from its limits) — soften it when a task needs large + # wrist reorientation. Damping follows critically (2*sqrt(kp)). + kp_null: float | None = None + # Task-space stiffness override [xyz, rpy]; None -> the controller's default + # (100,100,100, 30,30,30). MEASURED: at rot-gain 30 a wrist welded to a ~2.5 kg + # payload rolls ~16 deg toward a commanded 100 deg reorientation and equilibrates + # (free-space: 1 deg residual) — the rotation channel needs more authority under + # payload coupling. + task_prop_gains: tuple[float, ...] | None = None + franka_usd: str = "" # "" -> the vendored robots/assets/franka/panda_instanceable.usd + franka_urdf: str = "" # "" -> the vendored kinematics URDF (used by the pink_ik control mode) + + def __post_init__(self) -> None: + assets = asset_path(Path(__file__).resolve().parent / "assets") / "franka" + self.franka_usd = self.franka_usd or str(assets / "panda_instanceable.usd") + self.franka_urdf = self.franka_urdf or str(assets / "panda_kinematics.urdf") + + +@ROBOTS.register("franka") +class FrankaRobot(BaseRobot): + """Franka Panda arm + gripper. `apply_action` delegates to the controller for `control_mode`; the + 2 gripper fingers are always direct position targets, the 7 arm joints by the mode's arm controller + (torque-mode OSC, or position-mode JointController).""" + + control_modes: tuple[str, ...] = ("osc", "impedance", "diff_ik", "pink_ik", "joint") # osc default: smooth on this arm + cfg: FrankaRobotCfg + + ARM_JOINTS: tuple[str, ...] = ("panda_joint[1-7]",) + GRIPPER_JOINTS: tuple[str, ...] = ("panda_finger_joint.*",) + EE_BODY: str = "panda_hand" # the OSC control frame (a real body with a Jacobian) + + #: Eye-in-hand view at the standard RealSense D435 bracket pose (ManiSkill panda_wristcam / + #: DROID rig): beside the hand, looking parallel to the +z approach axis, ~90 deg FOV. + CAMERAS: ClassVar[dict[str, dict]] = { + "wrist": {"link": "panda_hand", "eye": (0.0465, -0.02, 0.036), "target": (0.0465, -0.02, 0.3), + "focal": 10.5}, + } + + # Action/target rate (s). Torque modes (osc/impedance): ~15 Hz target (Isaac `Factory-NutThread`, + # decim 8 @ 120 Hz), latched while the torque law recomputes every physics step (see + # `_TaskSpaceController.apply`). Joint mode: ~50 Hz, held by the actuator PD. + TORQUE_CONTROL_DT: float = 1.0 / 15.0 + JOINT_CONTROL_DT: float = 0.02 + + def __init__(self, cfg: FrankaRobotCfg | None = None) -> None: + super().__init__(cfg or FrankaRobotCfg()) + + # ----- assets ------------------------------------------------------------------------------- + def assets(self) -> dict[str, Any]: + """The Franka articulation (`FRANKA_PANDA_HIGH_PD_CFG`), base fixed per `cfg.fixed_base`, spawned + at the configured pose. In the torque modes ("impedance"/"osc") the arm actuators are set to + torque mode (zero stiffness/damping); in "diff_ik"/"joint" mode they keep position PD. The gripper is always PD.""" + from isaaclab_assets.robots.franka import FRANKA_PANDA_HIGH_PD_CFG + + c = self.cfg + robot = FRANKA_PANDA_HIGH_PD_CFG.copy() # type: ignore[attr-defined] + robot.prim_path = f"{{ENV_REGEX_NS}}/{self.prim_name}" # cfg.name-namespaced (default "Robot") + robot.spawn.usd_path = c.franka_usd # vendored local panda (resolved in cfg.__post_init__) + robot.spawn.articulation_props.fix_root_link = c.fixed_base + robot.init_state.pos = c.base_pos + robot.init_state.rot = c.base_rot + # Home the 7 arm joints to cfg.default_dof_pos (reset() reads this via default_joint_pos); + # keep the gripper-finger defaults. + robot.init_state.joint_pos = { + **robot.init_state.joint_pos, + **{f"panda_joint{i + 1}": float(q) for i, q in enumerate(c.default_dof_pos)}, + } + torque_mode = self.control_mode in ("impedance", "osc") + for arm_act in ("panda_shoulder", "panda_forearm"): + robot.actuators[arm_act].stiffness = 0.0 if torque_mode else c.arm_stiffness + robot.actuators[arm_act].damping = 0.0 if torque_mode else c.arm_damping + if c.arm_effort_limit is not None: + robot.actuators[arm_act].effort_limit_sim = c.arm_effort_limit + robot.actuators["panda_hand"].stiffness = c.gripper_stiffness + robot.actuators["panda_hand"].damping = c.gripper_damping + if c.gripper_effort_limit is not None: + robot.actuators["panda_hand"].effort_limit_sim = c.gripper_effort_limit + if c.gravity_compensation is not None: + # Newton-backend gravity compensation: swap in the MuJoCo rigid-body schema carrying + # `gravcomp` (imported lazily so the PhysX/2.x venv never touches isaaclab_newton). + from isaaclab_newton.sim.schemas.schemas_cfg import MujocoRigidBodyPropertiesCfg + + robot.spawn.rigid_props = MujocoRigidBodyPropertiesCfg(gravcomp=c.gravity_compensation) + if c.disable_arm_gravity: + robot.spawn.rigid_props.disable_gravity = True + return {self.name: robot} + + # ----- lifecycle (hooks; the base orchestrates bind -> on_bind -> build_controller) --------- + def on_bind(self, env: BaseEnv) -> None: + self.articulation: Articulation = env.iscene[self.name] + + def build_controller(self) -> CompositeController: + """`composite([, joint(gripper)])` for the active mode. The gripper is always a + position JointController (2 fingers); the arm controller is OSC (torque) or JointController + (position).""" + torque_mode = self.control_mode in ("impedance", "osc") + ctrl_dt = self.TORQUE_CONTROL_DT if torque_mode else self.JOINT_CONTROL_DT + gripper = JointController(JointControllerCfg(self.GRIPPER_JOINTS, dt=ctrl_dt), command_type="position") + if torque_mode: + ts_cfg = TaskSpaceControllerCfg( + dt=ctrl_dt, # target rate; the torque law itself recomputes every physics step (see apply) + ee_body=self.EE_BODY, + arm_joint_names=self.ARM_JOINTS, + nullspace_dof_pos=self.cfg.nullspace_dof_pos or self.cfg.default_dof_pos, # () -> home pose + ema_factor=0.2, # smooth the action stream + ) + if self.cfg.kp_null is not None: + ts_cfg.kp_null = self.cfg.kp_null + ts_cfg.kd_null = 2.0 * math.sqrt(self.cfg.kp_null) + if self.cfg.task_prop_gains is not None: + ts_cfg.task_prop_gains = tuple(self.cfg.task_prop_gains) + ts_cfg.task_deriv_gains = () # keep critical damping (2*sqrt(kp)) + cls = TaskSpaceImpedanceController if self.control_mode == "impedance" else OperationalSpaceController + arm: Any = cls(ts_cfg) + elif self.control_mode == "diff_ik": + # Same 6-D delta action as the torque modes; the DLS step emits joint position targets + # the arm PD (cfg.arm_stiffness/arm_damping, HIGH_PD 400/80) tracks. + arm = DiffIKController(DiffIKControllerCfg(dt=ctrl_dt, ee_body=self.EE_BODY, arm_joint_names=self.ARM_JOINTS)) + elif self.control_mode == "pink_ik": + # One frame task on the hand over the 7-joint chain (kinematics-only URDF; USD names == + # URDF names on the panda). Task costs = the G1's proven set (position 8/m, orientation + # 2/rad, gain 0.5, lm 10 — the FrameTaskCfg defaults); the 1-DOF elbow redundancy is + # anchored by the nullspace posture pull toward home. Emits joint position targets the + # same 400/80 arm PD tracks. + arm = PinkIKController( + PinkIKControllerCfg( + dt=ctrl_dt, + urdf_path=self.cfg.franka_urdf, + base_link="panda_link0", + base_link_frame="panda_link0", + frames=(FrameTaskCfg(self.EE_BODY),), + joint_names=self.ARM_JOINTS, + nullspace_joints=tuple(f"panda_joint{i}" for i in range(1, 8)), + ) + ) + elif self.control_mode == "joint": + arm = JointController(JointControllerCfg(self.ARM_JOINTS, dt=ctrl_dt), command_type="position") + else: + raise ValueError(f"unknown Franka control_mode {self.control_mode!r}; known: {self.control_modes}") + return CompositeController([arm, gripper]) + + def reset(self, env_ids: torch.Tensor) -> None: + """Home pose: default joint state; hold all joint position targets at default (the gripper PD + holds, the arm targets are inert in torque mode). Root written to the fixed spawn pose + origin.""" + art = self.articulation + jp = art.data.default_joint_pos[env_ids].clone() + jv = art.data.default_joint_vel[env_ids].clone() + art.write_joint_state_to_sim(jp, jv, env_ids=env_ids) + art.set_joint_position_target(jp, env_ids=env_ids) + art.set_joint_effort_target(torch.zeros_like(jp), env_ids=env_ids) + root = art.data.default_root_state[env_ids].clone() + root[:, 0:3] += self.env.iscene.env_origins[env_ids] + art.write_root_state_to_sim(root, env_ids) + if self.controller is not None: + self.controller.reset(env_ids) + + # NOTE: apply_action / action_dim are inherited from BaseRobot — they delegate to self.controller + # (composite of arm + gripper). The OSC arm writes effort, the gripper writes position, each through + # the sink it captured at bind. + + # get_state / set_state are inherited from BaseRobot (single-articulation sim state + controller). + + # ----- description -------------------------------------------------------------------------- + def describe(self) -> str: + mode = self.control_mode + if mode == "impedance": + arm = "7 arm joints by task-space impedance (joint torque); the action is 6 end-effector pose deltas" + elif mode == "osc": + arm = "7 arm joints by operational-space control (joint torque); the action is 6 end-effector pose deltas" + elif mode == "diff_ik": + arm = "7 arm joints by differential IK (joint position targets); the action is 6 end-effector pose deltas" + elif mode == "pink_ik": + arm = ("7 arm joints by Pink QP IK (joint position targets); the action is the absolute hand pose " + "[pos, quat wxyz] in the env frame") + else: + arm = "7 arm joints by direct position targets" + return ( + f"A Franka Emika Panda arm with a parallel-jaw gripper, fixed to the table. Control mode " + f"'{mode}': {arm}, plus 2 gripper fingers by direct position target. Action dim {self.action_dim}." + ) diff --git a/reference/robobench/suites/assembly/grader/allen_bolt_assembly.py b/reference/robobench/suites/assembly/grader/allen_bolt_assembly.py new file mode 100644 index 0000000000000000000000000000000000000000..53f5c4771d34d2501de9fc2b553d28fec2dc6929 --- /dev/null +++ b/reference/robobench/suites/assembly/grader/allen_bolt_assembly.py @@ -0,0 +1,77 @@ +"""Grader for the allen_bolt scene: every bolt driven home with the key. + +Rubric stages (weights and modes live in RUBRIC): + key_grasped a key has been taken in the weld-on-closure grip (`scene.grasp_held`, any + key, either arm) — the scene's own grasp state; a milestone, latched. Also + true once every bolt is seated (a seated bolt was necessarily driven by + the key), so the rung nests under `driven`. Under an embodiment with no + gripper (the weld contract disabled, e.g. robot="null") the grasp state does + not exist and the rung reads from that implication alone + driven mean over bolts of the tip's depth from the start register to the seat — + `bolt_stage_depth` -> `seat_depth` when the bolts spawn hand-started + (`bolt_staged`, every robot binding), plate top (depth 0) -> `seat_depth` + for the lying spawn — counted only while the bolt is within `align_xy` of the + hole axis and `align_axis_deg` of upright, the scene's own non-depth seat + gates, so a bolt's value is 1.0 iff the scene calls it seated +Both stages are per env. The key's own "seated in the hex socket" step is NOT a rung: the +head/socket geometry is not in the scene cfg, so it cannot be read from state without an +invented tolerance. Success is the scene's `success()` per env (every bolt `seated()`), +read when the delivery finishes. +""" + +from __future__ import annotations + +import math + +from robobench.core import BaseGrader +from robobench.suites.assembly.scenes.allen_bolt_assembly import AllenBoltAssemblyScene + + +class AllenBoltAssemblyGrader(BaseGrader): + """Every bolt seated — driven down to seat depth with the key, upright on the hole axis. + + Ladder: key_grasped 0.50 · driven 1.00 — two equal rungs for the two measurable steps + (take the key, drive the bolt home); the second is a linear ramp of tip depth from the + bolt's start register (`bolt_stage_depth`, or the plate top for a lying spawn) to + `seat_depth`. With N bolts `driven` is the mean over bolts. + """ + + SCENE = AllenBoltAssemblyScene + RUBRIC = (("key_grasped", 1, "once"), ("driven", 1)) + scene: AllenBoltAssemblyScene + + def setup(self) -> None: + import torch + + c = self.scene.cfg + self._depth0 = c.bolt_stage_depth if c.bolt_staged else 0.0 # tip depth at the start + # `grasp_held` exists only while the weld contract is live (a gripper on the stage). + self._has_grasp = hasattr(self.scene, "grasp_held") + self._grasped = torch.zeros(self.num_envs, dtype=torch.bool, + device=self.scene.engaged().device) # latch: a key ever held + + def check_success(self): + return self.scene.success() # (num_envs,) — every bolt of that env seated + + def _in_hole(self): + """(aligned, drive), each (num_envs, num_bolts): whether the bolt passes the scene's + xy + tilt seat gates in its nearest platform, and its tip-depth fraction from the + start register to the seat.""" + c = self.scene.cfg + off = self.scene._bolt_offsets_in_platform() # (n, B, P, 3) — privileged read + near_dist = off[..., :2].norm(dim=-1).min(dim=-1).values + aligned = (near_dist <= c.align_xy) & ( + self.scene._bolt_axis_cos() >= math.cos(math.radians(c.align_axis_deg))) + depth = self.scene.engaged() # (n, B) tip depth below the plate top (m) + drive = ((depth - self._depth0) / (c.seat_depth - self._depth0)).clamp(0.0, 1.0) + return aligned, drive + + # ---- rubric stages — each returns a (num_envs,) value in [0, 1] --------------------- + def key_grasped(self): + if self._has_grasp: + self._grasped |= self.scene.grasp_held.any(dim=1) + return (self._grasped | self.scene.success()).float() + + def driven(self): + aligned, drive = self._in_hole() + return (aligned * drive).mean(dim=1) diff --git a/reference/robobench/suites/assembly/grader/bulb_assembly.py b/reference/robobench/suites/assembly/grader/bulb_assembly.py new file mode 100644 index 0000000000000000000000000000000000000000..6d5fbdf7f760f10c2bea12e0ce1db221fd2edb43 --- /dev/null +++ b/reference/robobench/suites/assembly/grader/bulb_assembly.py @@ -0,0 +1,78 @@ +"""Grader for the bulb scene: every bulb seated. + +Rubric stages (weights and modes live in RUBRIC), each a fraction over that env's bulbs: + lifted bulbs ever raised to the socket's bore-mouth height — origin at/above + `socket_opening_z` in the nearest socket's frame, the height a cap-down bulb + has to clear before it can be set on the socket — or already in a bore (a bulb + in the bore was necessarily lifted there); a milestone, latched per bulb + engaged bulbs on/in a socket bore — within `align_xy` of the axis and at/below the + bore mouth (`socket_opening_z`) + threaded mean thread depth over the scene's own glow band, from `light_start_z` (free + rest on the thread, where the glow begins) to `seat_z` (seated), counted only + while the bulb is aligned in xy AND tilt exactly as the scene's `seated()` + demands — so a bulb's value is 1.0 iff the scene calls it seated +The rungs nest (seated => engaged => lifted), so progress reaches 1.0 exactly when every +bulb of the env is seated, and for a grader built after the pick as well as one that +watched it. Success is the scene's `seated()` per env, read when the delivery finishes. +""" + +from __future__ import annotations + +import math + +from robobench.core import BaseGrader +from robobench.suites.assembly.scenes.bulb_assembly import BulbAssemblyScene + + +class BulbAssemblyGrader(BaseGrader): + """Every bulb seated — threaded to depth, on the socket axis, upright. + + Ladder: lifted 0.33 · engaged 0.67 · threaded 1.00 — three equal rungs for the three + steps of the task (pick the bulb up, set it on the socket, screw it home); the last + rung is a linear ramp of thread depth over the scene's own glow band + (`light_start_z` -> `seat_z`). With N bulbs every stage is the fraction of bulbs. + """ + + SCENE = BulbAssemblyScene + RUBRIC = (("lifted", 1, "once"), ("engaged", 1), ("threaded", 1)) + scene: BulbAssemblyScene + + def setup(self) -> None: + import torch + + z, _, _, _ = self._in_socket() + self._lifted = torch.zeros_like(z, dtype=torch.bool) # per-bulb latch: ever lifted + + def check_success(self): + return self.scene.seated().all(dim=1) # (num_envs,) — every bulb of that env + + def _in_socket(self): + """(height, engaged, aligned, thread), each (num_envs, num_bulbs): the bulb origin's + height above its nearest socket's origin, whether it is in that bore, whether it + passes the scene's xy + tilt seat gates, and its thread-depth fraction.""" + import torch + + c = self.scene.cfg + off = self.scene._bulb_offsets_in_socket() # (n, N_bulb, B_socket, 3) — privileged read + near_dist, near = off[..., :2].norm(dim=-1).min(dim=-1) + z = torch.gather(off[..., 2], 2, near.unsqueeze(-1)).squeeze(-1) + xy_ok = near_dist <= c.align_xy + engaged = xy_ok & (z <= c.socket_opening_z) + aligned = xy_ok & (self.scene._bulb_axis_cos() >= math.cos(math.radians(c.align_axis_deg))) + thread = ((c.light_start_z - z) / (c.light_start_z - c.seat_z)).clamp(0.0, 1.0) + return z, engaged, aligned, thread + + # ---- rubric stages — each returns a (num_envs,) fraction over that + # env's bulbs ------------------------------------------------------------ + def lifted(self): + z, engaged, _, _ = self._in_socket() + self._lifted |= (z >= self.scene.cfg.socket_opening_z) | engaged + return self._lifted.float().mean(dim=1) + + def engaged(self): + _, engaged, _, _ = self._in_socket() + return engaged.float().mean(dim=1) + + def threaded(self): + _, _, aligned, thread = self._in_socket() + return (aligned * thread).mean(dim=1) diff --git a/reference/robobench/suites/assembly/grader/ikea_table_assembly.py b/reference/robobench/suites/assembly/grader/ikea_table_assembly.py new file mode 100644 index 0000000000000000000000000000000000000000..df8f3150c54e34793ace24e95c75e3b9140bfc4a --- /dev/null +++ b/reference/robobench/suites/assembly/grader/ikea_table_assembly.py @@ -0,0 +1,69 @@ +"""Grader for the ikea_table scene: all four legs screwed down onto the tabletop's studs. + +The four legs are identical sub-goals, so every rung is ONE stage whose value is the fraction +of legs (k/4) that have reached it. The scene has no grasp contract and exposes no threshold +for "lifted", so a grasped/lifted rung cannot be measured from state and is not one; per leg +the ladder is aligned -> seated, equal weights, all measured in the tabletop's own frame +(order-independent: any leg on any stud, as the scene's `seated()` is): + aligned legs standing on a stud — within `align_xy` of the nearest stud and screw axis + within `align_axis_deg` of the stud axis: the scene's own two non-depth seat gates + seated mean over legs of the screw-down from the stud-top rest height (`leg_start_z`, the + scene's on-stud spawn height) to the seat (`seat_z`), counted only while aligned — + so a leg's value is 1.0 iff the scene calls it `seated()` +The rungs nest (seated => aligned), so progress reaches 1.0 exactly when every leg is seated. +The scene welds a seated leg to the table, so a welded leg stays `seated()` however the +assembly is later moved. Success is the scene's own `success()` (every leg `seated()`), read +when the delivery finishes. +""" + +from __future__ import annotations + +import math + +from robobench.core import BaseGrader +from robobench.suites.assembly.scenes.ikea_table_assembly import IkeaTableAssemblyScene + + +class IkeaTableAssemblyGrader(BaseGrader): + """All four legs seated on the studs — each threaded down to depth, on a stud axis, upright. + + Ladder (per leg, each rung 1/8): aligned 1/8 · seated 2/8; + 1 leg 0.25 · 2 legs 0.50 · 3 legs 0.75 · 4 legs 1.00 — two equal rungs for the two + measurable steps of each leg (set it on its stud, screw it down); the second is a linear + ramp of the leg's height from the stud-top rest height (`leg_start_z`) to the seat (`seat_z`). + """ + + SCENE = IkeaTableAssemblyScene + # Equal weights: the two rungs of one leg's ladder; every stage is a k/4 fraction. + RUBRIC = (("aligned", 1), ("seated", 1)) + scene: IkeaTableAssemblyScene + + def setup(self) -> None: + pass # every rung reads the live state against the scene's cfg; nothing to capture + + def check_success(self): + return self.scene.success() # (num_envs,) — every leg of that env seated + + def _on_stud(self): + """(aligned, thread), each (num_envs, num_legs): whether the leg passes the scene's + xy + tilt seat gates at its nearest stud, and its screw-down fraction from the + stud-top rest height to the seat.""" + import torch + + c = self.scene.cfg + off = self.scene._leg_offsets_in_table() # (n, L, 3): leg position in the slab frame + studs = torch.tensor(c.slots, device=off.device, dtype=off.dtype) # (S, 2) + near = (off[:, :, None, :2] - studs[None, None]).norm(dim=-1).amin(dim=-1) + aligned = (near <= c.align_xy) & ( + self.scene._leg_axis_cos() >= math.cos(math.radians(c.align_axis_deg))) + thread = ((c.leg_start_z - off[..., 2]) / (c.leg_start_z - c.seat_z)).clamp(0.0, 1.0) + return aligned, thread + + # ---- rubric stages — each returns a (num_envs,) fraction over that env's legs ----------- + def aligned(self): + aligned, _ = self._on_stud() + return aligned.float().mean(dim=1) + + def seated(self): + aligned, thread = self._on_stud() + return (aligned * thread).mean(dim=1) diff --git a/reference/robobench/suites/assembly/grader/nut_thread_assembly.py b/reference/robobench/suites/assembly/grader/nut_thread_assembly.py new file mode 100644 index 0000000000000000000000000000000000000000..d83e6cf01129ce82b86c864a18547e05e3a1341a --- /dev/null +++ b/reference/robobench/suites/assembly/grader/nut_thread_assembly.py @@ -0,0 +1,77 @@ +"""Grader for the nut_thread scene: every nut seated on a bolt. + +Rubric stages (weights and modes live in RUBRIC), each a fraction over that env's nuts: + lifted nuts ever raised to the bolt-top rest height — origin at/above `bolt_height` + in the nearest bolt's frame, the height the scene quotes for a nut resting on + the bolt top and so the height a nut has to clear before it can be set on the + bolt — or already aligned on a bolt (a nut on the bolt was necessarily lifted + there); a milestone, latched per nut + aligned nuts aligned on a bolt — within `align_xy` of the bolt axis and tilted at most + `align_axis_deg` off it, the scene's own two non-depth seat gates + threaded mean thread depth from the bolt-top rest height (`bolt_height`) down to the + seat (`seat_z`), counted only while aligned — so a nut's value is 1.0 iff the + scene calls it seated +The rungs nest (seated => aligned => lifted), so progress reaches 1.0 exactly when every +nut of the env is seated, and for a grader built after the pick as well as one that watched +it. Success is the scene's `seated()` per env, read when the delivery finishes. +""" + +from __future__ import annotations + +import math + +from robobench.core import BaseGrader +from robobench.suites.assembly.scenes.nut_thread_assembly import NutThreadAssemblyScene + + +class NutThreadAssemblyGrader(BaseGrader): + """Every nut seated — threaded down to seat depth, on the bolt axis, upright. + + Ladder: lifted 0.33 · aligned 0.67 · threaded 1.00 — three equal rungs for the three + steps of the task (pick the nut up, set it on the bolt, screw it down); the last rung + is a linear ramp of the nut's height from the bolt-top rest height (`bolt_height`) to + the seat (`seat_z`). With N nuts every stage is the fraction of nuts. + """ + + SCENE = NutThreadAssemblyScene + RUBRIC = (("lifted", 1, "once"), ("aligned", 1), ("threaded", 1)) + scene: NutThreadAssemblyScene + + def setup(self) -> None: + import torch + + z, _, _ = self._on_bolt() + self._lifted = torch.zeros_like(z, dtype=torch.bool) # per-nut latch: ever lifted + + def check_success(self): + return self.scene.seated().all(dim=1) # (num_envs,) — every nut of that env + + def _on_bolt(self): + """(height, aligned, thread), each (num_envs, num_nuts): the nut origin's height + above its nearest bolt's origin, whether it passes the scene's xy + tilt seat gates, + and its thread-depth fraction from the bolt-top rest height to the seat.""" + import torch + + c = self.scene.cfg + off = self.scene._nut_offsets_in_bolt() # (n, N_nut, B_bolt, 3) — privileged read + near_dist, near = off[..., :2].norm(dim=-1).min(dim=-1) + z = torch.gather(off[..., 2], 2, near.unsqueeze(-1)).squeeze(-1) + aligned = (near_dist <= c.align_xy) & ( + self.scene._nut_axis_cos() >= math.cos(math.radians(c.align_axis_deg))) + thread = ((c.bolt_height - z) / (c.bolt_height - c.seat_z)).clamp(0.0, 1.0) + return z, aligned, thread + + # ---- rubric stages — each returns a (num_envs,) fraction over that + # env's nuts ------------------------------------------------------------- + def lifted(self): + z, aligned, _ = self._on_bolt() + self._lifted |= (z >= self.scene.cfg.bolt_height) | aligned + return self._lifted.float().mean(dim=1) + + def aligned(self): + _, aligned, _ = self._on_bolt() + return aligned.float().mean(dim=1) + + def threaded(self): + _, aligned, thread = self._on_bolt() + return (aligned * thread).mean(dim=1) diff --git a/reference/robobench/suites/assembly/grader/pc_gpu_assembly.py b/reference/robobench/suites/assembly/grader/pc_gpu_assembly.py new file mode 100644 index 0000000000000000000000000000000000000000..664333bd08f9dd78e29286c73de4d6225c63304f --- /dev/null +++ b/reference/robobench/suites/assembly/grader/pc_gpu_assembly.py @@ -0,0 +1,75 @@ +"""Grader for the pc_gpu scene: the graphics card seated in the PCIe x16 slot. + +Rubric stages (weights and modes live in RUBRIC): + grasped the card has been taken in the weld-on-closure grip (`scene.grasp_held`) — the + scene's own grasp state; a milestone, latched. Also true once the card is + aligned in the slot (a card in the slot was necessarily carried there), so the + rung nests under `aligned`. Under an embodiment with no gripper (the weld + contract disabled, e.g. robot="null") the grasp state does not exist and the + rung reads from that implication alone + aligned the card lined up on the slot — origin within `align_xy` of the seated point, + up axis within `align_axis_deg` of the slot axis, length axis within + `align_yaw_deg` of the slot heading: the scene's own three non-depth seat gates + pressed tab depth below the slot mouth (`slot_mouth_z`, the scene's own `engaged()`) + as a fraction of `seat_depth`, counted only while aligned — so it is 1.0 iff + the scene calls the card seated +Every stage is per env. The rungs nest (seated => aligned => grasped), so progress reaches +1.0 exactly when the card is seated, and for a grader built after the pick as well as one +that watched it. The rear-panel pass-through is NOT a rung: the cutout's geometry is not in +the scene cfg, so it cannot be read from state without an invented tolerance. Success is +the scene's `seated()` per env — depth, xy, tilt AND heading gates — read when the +delivery finishes. +""" + +from __future__ import annotations + +import math + +from robobench.core import BaseGrader +from robobench.suites.assembly.scenes.pc_gpu_assembly import PcGpuAssemblyScene + + +class PcGpuAssemblyGrader(BaseGrader): + """The card seated in its slot — pressed to depth, centred, upright, heading along the slot. + + Ladder: grasped 0.33 · aligned 0.67 · pressed 1.00 — three equal rungs for the three + steps of the task (take the card, line it up on the slot, press it home); the last rung + is a linear ramp of tab depth from the slot mouth (`slot_mouth_z`) to `seat_depth`. + """ + + SCENE = PcGpuAssemblyScene + RUBRIC = (("grasped", 1, "once"), ("aligned", 1), ("pressed", 1)) + scene: PcGpuAssemblyScene + + def setup(self) -> None: + import torch + + # `grasp_held` exists only while the weld contract is live (a gripper on the stage). + self._has_grasp = hasattr(self.scene, "grasp_held") + self._grasped = torch.zeros(self.num_envs, dtype=torch.bool, + device=self.scene.engaged().device) # latch: card ever held + + def check_success(self): + return self.scene.seated() # (num_envs,) + + def _aligned(self): + """(num_envs,) bool: the scene's xy + tilt + heading seat gates, without depth.""" + c = self.scene.cfg + rel = self.scene._card_offset_in_case() # (n, 3), zero at the seated pose — privileged + xy_ok = rel[:, :2].norm(dim=-1) <= c.align_xy + up_ok = self.scene._axis_cos(2) >= math.cos(math.radians(c.align_axis_deg)) + yaw_ok = self.scene._axis_cos(0) >= math.cos(math.radians(c.align_yaw_deg)) + return xy_ok & up_ok & yaw_ok + + # ---- rubric stages — each returns a (num_envs,) value in [0, 1] --------------------- + def grasped(self): + if self._has_grasp: + self._grasped |= self.scene.grasp_held.any(dim=1) + return (self._grasped | self._aligned()).float() + + def aligned(self): + return self._aligned().float() + + def pressed(self): + depth = self.scene.engaged() # (n,) tab depth below the slot mouth (m) + return self._aligned() * (depth / self.scene.cfg.seat_depth).clamp(0.0, 1.0) diff --git a/reference/robobench/suites/assembly/grader/pc_gpu_ram_assembly.py b/reference/robobench/suites/assembly/grader/pc_gpu_ram_assembly.py new file mode 100644 index 0000000000000000000000000000000000000000..ea650b71f071d3d61f6a36101cb82031b5392842 --- /dev/null +++ b/reference/robobench/suites/assembly/grader/pc_gpu_ram_assembly.py @@ -0,0 +1,115 @@ +"""Grader for the pc_gpu_ram scene: the graphics card AND both RAM sticks seated. + +Three parts — one card, two identical sticks. Each part climbs the same ladder, grasped -> +aligned -> seated, and every part is worth the same third of the score. The card has its own +three stages; the two identical sticks share three FRACTION stages (k/2 each), which +therefore carry weight 2 (one unit per stick) against the card's 1. + gpu_grasped / ram_grasped parts ever taken in the weld-on-closure grip (`scene.grasp_held`: + column 0 the card, 1.. the sticks, in `grasp_sites()` order) — + the scene's own grasp state; milestones, latched per part. Also + true for a part that is seated (a seated part was necessarily + carried there), so the rungs nest under the seated rungs. Under + an embodiment with no gripper (the weld contract disabled, e.g. + robot="null") the grasp state does not exist and the rungs read + from that implication alone + gpu_aligned / ram_aligned the part lined up on its slot — origin within `gpu_align_xy` / + `ram_align_xy` of the seated point, up axis within + `gpu_align_axis_deg` / `ram_align_axis_deg` of the slot axis, + length axis within `gpu_align_yaw_deg` / `ram_align_yaw_deg` of + the slot heading: the scene's own three non-depth seat gates + gpu_seated / ram_seated depth below the slot mouth (`gpu_slot_mouth_z` / `ram_slot_mouth_z`, + the scene's own `gpu_engaged()` / `ram_engaged()`) as a fraction + of `gpu_seat_depth` / `ram_seat_depth`, counted only while + aligned — so a part's value is 1.0 iff the scene calls it seated +The rungs nest (seated => aligned => grasped), so progress reaches 1.0 exactly when the card +and both sticks are seated, and for a grader built after the picks as well as one that +watched them. Success is the scene's own `success()` (card and every stick seated), read when +the delivery finishes. +""" + +from __future__ import annotations + +import math + +from robobench.core import BaseGrader +from robobench.suites.assembly.scenes.pc_gpu_ram_assembly import PcGpuRamAssemblyScene + + +class PcGpuRamAssemblyGrader(BaseGrader): + """The graphics card and both RAM sticks seated — each pressed to depth, centred, upright, heading along its slot. + + Ladder (total weight 9, every part worth 3/9): + card grasped 1/9 · card aligned 2/9 · card seated 3/9 (0.333) · + then per stick +1/9 per rung: first stick seated 6/9 (0.667) · both sticks seated 1.00. + Each part's seated rung is a linear ramp of depth from its slot mouth to its seat depth. + """ + + SCENE = PcGpuRamAssemblyScene + # Per-part accounting: the card's rungs weigh 1; the sticks' rungs are k/2 fractions over + # two identical parts, so they weigh 2 (one unit per stick) — every part counts the same. + RUBRIC = ( + ("gpu_grasped", 1, "once"), ("gpu_aligned", 1), ("gpu_seated", 1), + ("ram_grasped", 2, "once"), ("ram_aligned", 2), ("ram_seated", 2), + ) + scene: PcGpuRamAssemblyScene + + def setup(self) -> None: + import torch + + # `grasp_held` exists only while the weld contract is live (a gripper on the stage). + self._has_grasp = hasattr(self.scene, "grasp_held") + depth = self.scene.engaged() # (n, 1 + S): card, then the sticks + self._grasped = torch.zeros_like(depth, dtype=torch.bool) # per-part latch: ever held + + def check_success(self): + return self.scene.success() # (num_envs,) — the card and every stick of that env seated + + def _latch_grasped(self): + """(num_envs, 1 + num_slots) bool: ever held OR seated, card first then the sticks.""" + if self._has_grasp: + self._grasped |= self.scene.grasp_held[:, : 1 + self.scene.cfg.num_slots] + return self._grasped | self.scene.seated() + + def _gpu_aligned(self): + """(num_envs,) bool: the card passes the scene's xy + tilt + heading gates, without depth.""" + c = self.scene.cfg + rel = self.scene._card_offset_in_case() # (n, 3), zero at the seated pose — privileged + xy_ok = rel[:, 0:2].norm(dim=-1) <= c.gpu_align_xy + up_ok = self.scene._part_axis_cos(self.scene.card, 2) >= math.cos(math.radians(c.gpu_align_axis_deg)) + yaw_ok = self.scene._part_axis_cos(self.scene.card, 0) >= math.cos(math.radians(c.gpu_align_yaw_deg)) + return xy_ok & up_ok & yaw_ok + + def _ram_aligned(self): + """(num_envs, num_slots) bool: each stick passes the scene's xy + tilt + heading gates.""" + import torch + + c = self.scene.cfg + rel = self.scene._ram_offsets_in_case() # (n, S, 3), zero at the seated poses — privileged + xy_ok = rel[..., 0:2].norm(dim=-1) <= c.ram_align_xy + up_ok = torch.stack([self.scene._part_axis_cos(r, 2) for r in self.scene.rams], dim=1) >= math.cos( + math.radians(c.ram_align_axis_deg)) + yaw_ok = torch.stack([self.scene._part_axis_cos(r, 1) for r in self.scene.rams], dim=1) >= math.cos( + math.radians(c.ram_align_yaw_deg)) + return xy_ok & up_ok & yaw_ok + + # ---- card stages — each returns a (num_envs,) value in [0, 1] ----------------------------- + def gpu_grasped(self): + return self._latch_grasped()[:, 0].float() + + def gpu_aligned(self): + return self._gpu_aligned().float() + + def gpu_seated(self): + depth = self.scene.gpu_engaged() # (n,) tab depth below the PCIe slot mouth (m) + return self._gpu_aligned() * (depth / self.scene.cfg.gpu_seat_depth).clamp(0.0, 1.0) + + # ---- stick stages — each returns a (num_envs,) fraction over that env's sticks ----------- + def ram_grasped(self): + return self._latch_grasped()[:, 1:].float().mean(dim=1) + + def ram_aligned(self): + return self._ram_aligned().float().mean(dim=1) + + def ram_seated(self): + depth = self.scene.ram_engaged() # (n, S) blade depth below each DIMM slot mouth (m) + return (self._ram_aligned() * (depth / self.scene.cfg.ram_seat_depth).clamp(0.0, 1.0)).mean(dim=1) diff --git a/reference/robobench/suites/assembly/grader/pc_motherboard_assembly.py b/reference/robobench/suites/assembly/grader/pc_motherboard_assembly.py new file mode 100644 index 0000000000000000000000000000000000000000..5326226c49067ff16d73caa4e448b65529b3190b --- /dev/null +++ b/reference/robobench/suites/assembly/grader/pc_motherboard_assembly.py @@ -0,0 +1,82 @@ +"""Grader for the pc_motherboard scene: all seven case-mount bolts driven home with the key. + +Rubric stages (weights and modes live in RUBRIC): + key_grasped the key has been taken in the weld-on-closure grip (`scene.grasp_held`, whose + only site is the key handle) — the scene's own grasp state; a milestone, + latched. Also true once every bolt is seated (a seated bolt was necessarily + driven by the key), so the rung nests under `driven`. Under an embodiment + with no gripper (the weld contract disabled, e.g. robot="null") the grasp + state does not exist and the rung reads from that implication alone + driven mean over the seven bolts of the tip's depth from the start register to the + seat — `stage_depth` -> `seat_depth` under the screw mechanic (every bolt + spawns hand-started that deep), board face (depth 0) -> `seat_depth` when + the mechanic is off and a loose bolt is threaded in from the top — counted + only while the bolt is within `align_xy` of its nearest hole axis and + `align_axis_deg` of upright, the scene's own non-depth seat gates, so a + bolt's value is 1.0 iff the scene calls it `seated()` +Both stages are per env. The bolts spawn pre-staged in their holes, so an "aligned" rung +would be free at reset and is not one; the key's own "seated in the hex socket" step is NOT a +rung either: the head/socket geometry is not in the scene cfg, so it cannot be read from +state without an invented tolerance. Success is the scene's own `success()` (every bolt +`seated()`), read when the delivery finishes. +""" + +from __future__ import annotations + +import math + +from robobench.core import BaseGrader +from robobench.suites.assembly.scenes.pc_motherboard_assembly import PcMotherboardAssemblyScene + + +class PcMotherboardAssemblyGrader(BaseGrader): + """All seven motherboard bolts seated — each driven to depth on its hole axis, upright. + + Ladder (total weight 8, one unit per part — the key and each of the 7 bolts): + key grasped 0.125 · 1 bolt 0.25 · 2 bolts 0.375 · 3 bolts 0.50 · 4 bolts 0.625 · + 5 bolts 0.75 · 6 bolts 0.875 · 7 bolts 1.00. `driven` is the mean over bolts of a linear + ramp of tip depth from the start register (`stage_depth`, or the board face for a loose + spawn) to `seat_depth`. + """ + + SCENE = PcMotherboardAssemblyScene + # One unit per part: the key's single rung weighs 1; `driven` is a k/7 fraction over seven + # identical bolts and so weighs 7 (one unit per bolt). + RUBRIC = (("key_grasped", 1, "once"), ("driven", 7)) + scene: PcMotherboardAssemblyScene + + def setup(self) -> None: + import torch + + c = self.scene.cfg + self._depth0 = c.stage_depth if c.screw_mechanic else 0.0 # tip depth at the start + # `grasp_held` exists only while the weld contract is live (a gripper on the stage). + self._has_grasp = hasattr(self.scene, "grasp_held") + self._grasped = torch.zeros(self.num_envs, dtype=torch.bool, + device=self.scene.engaged().device) # latch: key ever held + + def check_success(self): + return self.scene.success() # (num_envs,) — every bolt of that env seated + + def _in_hole(self): + """(aligned, drive), each (num_envs, num_holes): whether the bolt passes the scene's + xy + tilt seat gates at its nearest hole, and its tip-depth fraction from the start + register to the seat.""" + c = self.scene.cfg + off = self.scene._bolt_offsets_in_case() # (n, B, H, 3) — privileged read + near_dist = off[..., :2].norm(dim=-1).min(dim=-1).values + aligned = (near_dist <= c.align_xy) & ( + self.scene._bolt_axis_cos() >= math.cos(math.radians(c.align_axis_deg))) + depth = self.scene.engaged() # (n, B) tip depth below the board face (m) + drive = ((depth - self._depth0) / (c.seat_depth - self._depth0)).clamp(0.0, 1.0) + return aligned, drive + + # ---- rubric stages — each returns a (num_envs,) value in [0, 1] ---------------------------- + def key_grasped(self): + if self._has_grasp: + self._grasped |= self.scene.grasp_held.any(dim=1) + return (self._grasped | self.scene.success()).float() + + def driven(self): + aligned, drive = self._in_hole() + return (aligned * drive).mean(dim=1) diff --git a/reference/robobench/suites/assembly/grader/pc_ram_assembly.py b/reference/robobench/suites/assembly/grader/pc_ram_assembly.py new file mode 100644 index 0000000000000000000000000000000000000000..8d16a44f07d021c4e145828c550a4ad30b51ca83 --- /dev/null +++ b/reference/robobench/suites/assembly/grader/pc_ram_assembly.py @@ -0,0 +1,77 @@ +"""Grader for the pc_ram scene: both RAM sticks seated in their DIMM slots. + +The two sticks are identical sub-goals, so every rung is ONE stage whose value is the +fraction of sticks (k/2) that have reached it. Per stick the ladder is grasped -> aligned -> +seated, equal weights: + grasped sticks ever taken in the weld-on-closure grip (`scene.grasp_held`, one column + per stick) — the scene's own grasp state; a milestone, latched per stick. Also + true for a stick that is seated (a seated stick was necessarily carried there), + so the rung nests under `seated`. Under an embodiment with no gripper (the weld + contract disabled, e.g. robot="null") the grasp state does not exist and the + rung reads from that implication alone + aligned sticks lined up on their slot — origin within `align_xy` of the seated point, up + axis within `align_axis_deg` of the slot axis, length axis within `align_yaw_deg` + of the slot heading: the scene's own three non-depth seat gates + seated mean over sticks of the blade depth below the slot mouth (`slot_mouth_z`, the + scene's own `engaged()`) as a fraction of `seat_depth`, counted only while + aligned — so a stick's value is 1.0 iff the scene calls it `seated()` +The rungs nest (seated => aligned => grasped), so progress reaches 1.0 exactly when both +sticks are seated, and for a grader built after the picks as well as one that watched them. +Success is the scene's own `success()` (every stick `seated()`), read when the delivery +finishes. +""" + +from __future__ import annotations + +import math + +from robobench.core import BaseGrader +from robobench.suites.assembly.scenes.pc_ram_assembly import PcRamAssemblyScene + + +class PcRamAssemblyGrader(BaseGrader): + """Both sticks seated — pressed to depth, centred, upright, heading along the slot. + + Ladder (per stick, each rung 1/6): grasped 1/6 · aligned 2/6 · seated 3/6; + one stick done 0.50 · both sticks done 1.00 — three equal rungs for the three steps of + each stick (take it, line it up on its slot, press it home); the last rung is a linear + ramp of blade depth from the slot mouth (`slot_mouth_z`) to `seat_depth`. + """ + + SCENE = PcRamAssemblyScene + # Equal weights: the three rungs of one stick's ladder; every stage is a k/2 fraction. + RUBRIC = (("grasped", 1, "once"), ("aligned", 1), ("seated", 1)) + scene: PcRamAssemblyScene + + def setup(self) -> None: + import torch + + # `grasp_held` exists only while the weld contract is live (a gripper on the stage). + self._has_grasp = hasattr(self.scene, "grasp_held") + depth = self.scene.engaged() # (n, S) + self._grasped = torch.zeros_like(depth, dtype=torch.bool) # per-stick latch: ever held + + def check_success(self): + return self.scene.success() # (num_envs,) — every stick of that env seated + + def _aligned(self): + """(num_envs, num_slots) bool: the scene's xy + tilt + heading seat gates, without depth.""" + c = self.scene.cfg + rel = self.scene._ram_offsets_in_case() # (n, S, 3), zero at the seated poses — privileged + xy_ok = rel[..., 0:2].norm(dim=-1) <= c.align_xy + up_ok = self.scene._axis_cos(2) >= math.cos(math.radians(c.align_axis_deg)) + yaw_ok = self.scene._axis_cos(1) >= math.cos(math.radians(c.align_yaw_deg)) + return xy_ok & up_ok & yaw_ok + + # ---- rubric stages — each returns a (num_envs,) fraction over that env's sticks -------- + def grasped(self): + if self._has_grasp: + self._grasped |= self.scene.grasp_held[:, : self.scene.cfg.num_slots] + return (self._grasped | self.scene.seated()).float().mean(dim=1) + + def aligned(self): + return self._aligned().float().mean(dim=1) + + def seated(self): + depth = self.scene.engaged() # (n, S) blade depth below the slot mouth (m) + return (self._aligned() * (depth / self.scene.cfg.seat_depth).clamp(0.0, 1.0)).mean(dim=1) diff --git a/reference/robobench/suites/assembly/grader/so101_assembly.py b/reference/robobench/suites/assembly/grader/so101_assembly.py new file mode 100644 index 0000000000000000000000000000000000000000..4ff7649abda32b6cb90b423828abe2ad3aea9692 --- /dev/null +++ b/reference/robobench/suites/assembly/grader/so101_assembly.py @@ -0,0 +1,121 @@ +"""Grader for the so101 scene: the elbow servo seated + tab-screwed into the upper arm, the forearm +clipped onto its horn + horn-screwed. + +The scene defines no `success()`; the criterion below is read off its own assembly geometry +and fastening state (see `check_success`): + every loose screw fastened — the four identical M2 tab screws in the four tab holes and the + eight identical M3 horn screws in the eight horn-line holes (the scene's own `state == 2`, + "fully fastened") — with the servo live in the arm pocket and the forearm live on the horn. + +Rubric stages (weights and modes live in RUBRIC): + motor_seated the bare servo sits in the upper arm's pocket: its body frame on the + upper_arm link frame (identical by construction when seated) within the + scene's fastening-gate tolerances `motor_align_pos` / `motor_align_deg` + — the scene's own PARTS ALIGNED rule for the tab holes + tab_screws_picked fraction of the four identical M2 tab screws ever carried on the drill's + magnetic bit (`attached`, the scene's only "grasp" of a screw; a + fastened screw was necessarily driven off that bit) — a milestone + tab_screws_fastened fraction of the four M2s welded home in a tab hole (`fastened`) + fork_seated the forearm's clevis sits on the servo's output horn: the lower_arm at + the elbow-joint seat transform of the motor, within the same + `motor_align_pos` / `motor_align_deg` gate, angle-agnostic about the + horn axis (the assembled elbow may stand at any angle) — the scene's + PARTS ALIGNED rule for the horn holes + horn_screws_picked fraction of the eight identical M3 horn screws ever carried on the bit + horn_screws_fastened fraction of the eight M3s welded home in a horn-line hole +Every stage is per env. The two "picked" milestones are the grasped rung of the screws' ladder; +the servo and the forearm have no grasp the scene can measure (no grasp contract, no lift +threshold in cfg), so their ladders start at the seated rung. A screw's transient "in the hole, +not yet home" state is not a rung: the auto-driver takes it from engaged to fastened by itself. +""" + +from __future__ import annotations + +from robobench.core import BaseGrader +from robobench.suites.assembly.scenes.so101_assembly import SO101AssemblyScene + + +class SO101AssemblyGrader(BaseGrader): + """The SO101 elbow assembled: servo seated and tab-screwed into the upper arm, forearm clipped onto the horn and horn-screwed. + + Ladder (14 units — one unit per part: servo 1, 4 M2 tab screws 4, forearm 1, 8 M3 horn + screws 8; a screw's unit split equally over its two rungs, picked 0.5 + fastened 0.5): + servo in pocket 1/14 = 0.071 · 4 M2 on the bit 3/14 = 0.214 · 4 M2 fastened 5/14 = 0.357 · + forearm on horn 6/14 = 0.429 · 8 M3 on the bit 10/14 = 0.714 · 8 M3 fastened 14/14 = 1.000 + The identical screws of each group share one k/N fraction stage weighted N/2 per rung. + """ + + SCENE = SO101AssemblyScene + # one unit per part; a screw's unit split equally over its two rungs (picked / fastened), + # so a group of N identical screws weighs N/2 on each rung + RUBRIC = ( + ("motor_seated", 1), + ("tab_screws_picked", 2, "once"), + ("tab_screws_fastened", 2), + ("fork_seated", 1), + ("horn_screws_picked", 4, "once"), + ("horn_screws_fastened", 4), + ) + scene: SO101AssemblyScene + + def setup(self) -> None: + import torch + + # per-screw latch: ever rode the magnetic bit (or was fastened, which requires it) + self._ever_on_bit = torch.zeros(self.num_envs, self.scene.cfg.num_screws, + dtype=torch.bool, device=self.env.device) + + def check_success(self): + """Every loose screw fastened (the scene's `state == 2`) AND the servo live in the + pocket AND the forearm live on the horn — the assembled elbow, on the final state.""" + all_fastened = (self.scene.fastened >= 0).all(dim=1) + return all_fastened & self._motor_seated() & self._fork_seated() + + # ---- predicates (the scene's own PARTS ALIGNED gates) ------------------------------------ + def _motor_seated(self): + import math + + from isaaclab.utils.math import quat_error_magnitude + + s, c = self.scene, self.scene.cfg + ap, aq = s.upper_arm_pose() + return (((s.motor.data.root_pos_w - ap).norm(dim=-1) < c.motor_align_pos) + & (quat_error_magnitude(s.motor.data.root_quat_w, aq) + < math.radians(c.motor_align_deg))) + + def _fork_seated(self): + import math + + s, c = self.scene, self.scene.cfg + lp, lq = s.lower_arm_pose() + seat_p, seat_q = s.lower_arm_seat_w() + _, off_axis = s._elbow_angle_split(lq, seat_q) # privileged: residual off the horn axis + return (((lp - seat_p).norm(dim=-1) < c.motor_align_pos) + & (off_axis < math.radians(c.motor_align_deg))) + + def _picked(self, lo: int, hi: int): + s = self.scene + self._ever_on_bit |= s.attached | (s.fastened >= 0) + return self._ever_on_bit[:, lo:hi].float().mean(dim=1) + + def _fastened(self, lo: int, hi: int): + return (self.scene.fastened[:, lo:hi] >= 0).float().mean(dim=1) + + # ---- rubric stages — each returns a (num_envs,) value in [0, 1] -------------------------- + def motor_seated(self): + return self._motor_seated().float() + + def tab_screws_picked(self): + return self._picked(0, self.scene.cfg.num_elbow_screws) + + def tab_screws_fastened(self): + return self._fastened(0, self.scene.cfg.num_elbow_screws) + + def fork_seated(self): + return self._fork_seated().float() + + def horn_screws_picked(self): + return self._picked(self.scene.cfg.num_elbow_screws, self.scene.cfg.num_screws) + + def horn_screws_fastened(self): + return self._fastened(self.scene.cfg.num_elbow_screws, self.scene.cfg.num_screws) diff --git a/reference/robobench/suites/cutting/grader/dice_food.py b/reference/robobench/suites/cutting/grader/dice_food.py new file mode 100644 index 0000000000000000000000000000000000000000..78aba52138e729bf94e2c0cad9a3475e0711cc74 --- /dev/null +++ b/reference/robobench/suites/cutting/grader/dice_food.py @@ -0,0 +1,71 @@ +"""Grader for the dice scene: the food diced into its target number of pieces, all still on the board. + +Rubric stages (weights and modes live in RUBRIC): + knife_taken the knife has been lifted off its rest — its root raised by the rest's rail + height (`cfg.rest_rail_h`), the lift that frees the plate from the notches; a + milestone, credit kept after the knife is put down + pairs_cut fraction of the grid's weld PAIRS released (`scene.pair_cut`, the dice gate's + own truth). The x-planes and y-planes are identical sub-goals — one stage, one + fraction, each pair worth the same + diced the scene's own done test: connected components of the live weld graph reach + the target piece count AND every piece is still on the board (footprint from + `cfg.board_size`, resting above the board top). "On the board" is a GATE on + this stage, not a stage of its own — it is true at reset and only means + anything once the food is diced +Success = `diced` read on the final state. +""" + +from __future__ import annotations + +from robobench.core import BaseGrader +from robobench.suites.cutting.scenes.dice_food import DiceFoodScene + + +class DiceFoodGrader(BaseGrader): + """Food diced into the target number of pieces, every piece still on the board. + + Ladder: knife taken 1/14 (0.071) · each weld pair released +1/14 (12 pairs -> 13/14 = + 0.929) · diced with every piece on the board 1.00 + """ + + SCENE = DiceFoodScene + # One weight unit per step of the task: taking the knife (1), releasing each of the 3 x 3 + # grid's 12 weld pairs (12 identical sub-goals -> one fraction stage carrying 12 units, so + # a single cut is worth exactly as much as the pick), and the final done gate (1). + RUBRIC = (("knife_taken", 1, "once"), ("pairs_cut", 12), ("diced", 1)) + scene: DiceFoodScene + + def setup(self) -> None: + self._knife_z0 = self.scene.knife.data.root_pos_w[:, 2].clone() # resting in the notches + + def check_success(self): + return self._diced() + + def _target_pieces(self) -> int: + return self.scene.cfg.target_pieces or len(self.scene.pieces) + + def _on_board(self): + """(num_envs,) bool: every piece's origin inside the board footprint and above its top.""" + import torch + + s = self.scene + hx, hy = s.cfg.board_size[0] / 2, s.cfg.board_size[1] / 2 + ok = torch.ones(s.env.num_envs, dtype=torch.bool, device=s.env.device) + for pc in s.pieces: + rel = pc.data.root_pos_w - s.env_origins + ok &= (rel[:, 0].abs() < hx) & (rel[:, 1].abs() < hy) & (rel[:, 2] > s._board_top) + return ok + + def _diced(self): + return (self.scene.pieces_count() >= self._target_pieces()) & self._on_board() + + # ---- rubric stages — each returns a (num_envs,) value in [0, 1] ----------------------- + def knife_taken(self): + z = self.scene.knife.data.root_pos_w[:, 2] + return (z > self._knife_z0 + self.scene.cfg.rest_rail_h).float() + + def pairs_cut(self): + return self.scene.pair_cut.float().mean(dim=1) + + def diced(self): + return self._diced().float() diff --git a/reference/robobench/suites/cutting/grader/slice_food.py b/reference/robobench/suites/cutting/grader/slice_food.py new file mode 100644 index 0000000000000000000000000000000000000000..1474b5a4f14d29987a708c563c2e69687fadf7e9 --- /dev/null +++ b/reference/robobench/suites/cutting/grader/slice_food.py @@ -0,0 +1,91 @@ +"""Grader for the slice scene: the food cut into its target number of pieces, all still on the board. + +Rubric stages (weights and modes live in RUBRIC): + knife_taken the knife has been lifted clear of its rest — its root raised by at least + cfg.rest_rail_h (the rails' height) above where it lay at the graded reset, so + the edge is above the rail tops and the notches no longer hold it; a milestone + ("once"), credit kept after the knife is set down again + planes_cut fraction of the cuts the target needs that have been made — released planes + over (target - 1), a linear ramp from the welded food (0) to the target piece + count (1), target = cfg.target_pieces or every piece. The scored planes are + identical sub-goals: one stage, one fraction, each plane worth the same + sliced the scene's own done test: piece count (connected components of the live weld + chain, `pieces_count()`) reaches the target AND every piece is still on the + chopping board — inside the board's footprint (cfg.board_size) and above its + top. "On the board" is a GATE on this stage, not a stage of its own: it is + true at reset and only means anything once the food is cut +Success = `sliced` read when the delivery finishes. +""" + +from __future__ import annotations + +from robobench.core import BaseGrader +from robobench.suites.cutting.scenes.slice_food import SliceFoodScene + + +class SliceFoodGrader(BaseGrader): + """Food sliced into the target number of pieces, every piece still on the board. + + Ladder (7 scored planes — the carrot / banana assets — target = all pieces): knife taken + 1/9 (0.111) · each plane cut +1/9 (7 planes -> 8/9 = 0.889) · sliced with every piece on + the board 1.00. + """ + + SCENE = SliceFoodScene + # One weight unit per step of the task: taking the knife (1), releasing each scored plane + # (7 identical sub-goals -> one fraction stage carrying 7 units, so a single cut is worth + # exactly as much as the pick), and the final done gate (1). The class default is the 7 + # planes of the carrot / banana assets; __init__ re-derives the unit count from the + # graded scene's own target so the rule holds for any food or `target_pieces`. + RUBRIC = (("knife_taken", 1, "once"), ("planes_cut", 7), ("sliced", 1)) + scene: SliceFoodScene + + def __init__(self, env) -> None: + if isinstance(env.scene, self.SCENE): # else leave the default; BaseGrader raises + cuts = self._cuts_needed(env.scene) + self.RUBRIC = (("knife_taken", 1, "once"), ("planes_cut", cuts), ("sliced", 1)) + super().__init__(env) + + @staticmethod + def _cuts_needed(scene: SliceFoodScene) -> int: + target = scene.cfg.target_pieces or len(scene.pieces) + if target <= 1: + raise ValueError(f"target_pieces={target} needs no cut — the planes ramp " + "(target - 1 cuts) is undefined") + return target - 1 + + def setup(self) -> None: + self._knife_z0 = self.scene.knife.data.root_pos_w[:, 2].clone() # resting on the rails + self._target = self._cuts_needed(self.scene) + 1 + + def check_success(self): + return self._sliced() + + def _on_board(self): + """(num_envs,) bool: every piece's root inside the board's footprint and above its top. + The kinematic board is centred on the env origin (assets(): pos (0, 0)), so its + half-extents are the test; a piece whose centre leaves the footprint tips off.""" + import torch + + s = self.scene + hx, hy = s.cfg.board_size[0] / 2, s.cfg.board_size[1] / 2 + ok = torch.ones(s.env.num_envs, dtype=torch.bool, device=s.env.device) + for pc in s.pieces: + rel = pc.data.root_pos_w - s.env_origins + ok &= (rel[:, 0].abs() < hx) & (rel[:, 1].abs() < hy) & (rel[:, 2] > s._board_top) + return ok + + def _sliced(self): + return (self.scene.pieces_count() >= self._target) & self._on_board() + + # ---- rubric stages — each returns a (num_envs,) value in [0, 1] --------------------- + def knife_taken(self): + rise = self.scene.knife.data.root_pos_w[:, 2] - self._knife_z0 + return (rise >= self.scene.cfg.rest_rail_h).float() + + def planes_cut(self): + made = (self.scene.pieces_count() - 1).float() + return (made / (self._target - 1)).clamp(0.0, 1.0) + + def sliced(self): + return self._sliced().float() diff --git a/reference/robobench/suites/deformable/grader/dumpling.py b/reference/robobench/suites/deformable/grader/dumpling.py new file mode 100644 index 0000000000000000000000000000000000000000..f4f1dc03624041b30430b73aa5208999c22a3fd9 --- /dev/null +++ b/reference/robobench/suites/deformable/grader/dumpling.py @@ -0,0 +1,97 @@ +"""Grader for the dumpling scene: the dough ball rolled out into a thin, wide, compact wrapper. + +Rubric stages (weights and modes live in RUBRIC): + flattened linear ramp of the scene's `flatten_height()` (q95 dough height above the table) + from its value at the graded reset (the ball, ~0.044 m) down to + `cfg.flatten_h95_max` + spread linear ramp of the scene's `spread_radius()` (q90 radius about the dough centroid) + from its reset value (~0.020 m) up to `cfg.spread_r90_min` + rolled the scene's own success: thin (flatten_h95_max) AND wide (spread_r90_min) AND the + guards — compact (`final_extent() <= extent_max`), conserved + (`conservation() >= conserve_min`), nothing punched through the table + (`min_particle_z() >= surface_z - floor_z_slack`). The guards are sanity predicates + on the done rung, not weighted rungs (they hold from the first step) + plausible the VLM plausibility gate on the rendered final frame (final rung): one round, + flat, even wrapper on the board with the pin set aside — rejects splats and + irregular shapes, torn/multiple pieces, dough on the pin or the pin in the dough, + dough off the board +Every stage is per env (the MPM scene is single-env). Success = the scene's `success()` on the +final state AND the plausibility gate. + +No pin-grasp rung: the scene's success does not involve the pin (dough flattened by any means +passes), so a grasp milestone could leave progress below 1 on an official success. +Not measurable from state (hence the gate): roundness/evenness of the sheet and whether the pin +is set aside — thin + wide + compact is also satisfied by a splat hammered with the pin's end. +""" + +from __future__ import annotations + +from robobench.core import BaseGrader +from robobench.suites.deformable.scenes.dumpling import DumplingScene + +GATE_PROMPT = ( + "Task: roll a ball of pale dough out into a dumpling wrapper on a dark worktop using a wooden " + "rolling pin (the dough is rendered as dense pale particles). A plausible completed final scene " + "shows a single round or roughly round, flat, even sheet of dough lying on the worktop, and the " + "rolling pin set aside — resting in its cradle or on the worktop, not on the dough. Reject if any " + "of these is visible: the dough is an irregular splat, smear or streak rather than a compact " + "round sheet; the dough is torn into several pieces or has holes; dough is stuck to the rolling " + "pin, or the pin is embedded in or resting on the dough; dough is off the worktop or on the " + "floor; the dough is still a thick ball or lump. Answer only about what is visible." +) + + +class DumplingGrader(BaseGrader): + """The dough rolled out into a thin, wide, compact wrapper on the board — the scene's gates met — judged plausible. + + Ladder: flattened 0.25 · spread 0.50 · rolled (scene gates) 0.75 · gate 1.00 + """ + + SCENE = DumplingScene + RUBRIC = (("flattened", 1), ("spread", 1), ("rolled", 1), ("plausible", 1, "final")) + # Final-frame camera (env-local meters): three-quarter view over the dough at (0, 0) and the + # pin cradle at (-0.14, -0.20) on the table top (z = 0.2), the whole board in frame. + CAMERA_EYE = (0.45, -0.60, 0.75) + CAMERA_TARGET = (-0.02, -0.05, 0.21) + scene: DumplingScene + + def setup(self) -> None: + from .vlm_judge import open_capture + + c = self.scene.cfg + self._h0 = self.scene.flatten_height().clone() # the ball's q95 height at the graded reset + self._r0 = self.scene.spread_radius().clone() # the ball's q90 radius at the graded reset + if not bool((self._h0 > c.flatten_h95_max).all() and (self._r0 < c.spread_r90_min).all()): + raise RuntimeError("dumpling grader: the reset dough already meets a wrapper gate " + f"(h95 {self._h0.tolist()} vs max {c.flatten_h95_max}, " + f"r90 {self._r0.tolist()} vs min {c.spread_r90_min})") + # Viewport capture for the final frame — attached now (right after the graded reset, + # when the render graph wires reliably); a failure is printed and retried at verdict. + self._capture = open_capture(self.env, "dumpling grader") + + def check_success(self): + return self.scene.success() + + # ---- rubric stages — each returns a (num_envs,) value in [0, 1] --------------------------- + def flattened(self): + h = self.scene.flatten_height() + return ((self._h0 - h) / (self._h0 - self.scene.cfg.flatten_h95_max)).clamp(0.0, 1.0) + + def spread(self): + r = self.scene.spread_radius() + return ((r - self._r0) / (self.scene.cfg.spread_r90_min - self._r0)).clamp(0.0, 1.0) + + def rolled(self): + return self.scene.success().float() + + def plausible(self): + from .vlm_judge import gate_final_frames + + def prepare() -> None: + # The Kit render shows the MPM dough frozen at spawn unless its positions are pushed + # into Fabric (the scene's own workaround) — do it once before the final frame. + self.scene.setup_particle_visuals() + self.scene.push_particle_visuals() + + return gate_final_frames(self.env, self._capture, GATE_PROMPT, + self.CAMERA_EYE, self.CAMERA_TARGET, prepare=prepare) diff --git a/reference/robobench/suites/deformable/grader/latte.py b/reference/robobench/suites/deformable/grader/latte.py new file mode 100644 index 0000000000000000000000000000000000000000..4545a2e2c4fd31e9a2143d5dd152db8140a573f0 --- /dev/null +++ b/reference/robobench/suites/deformable/grader/latte.py @@ -0,0 +1,92 @@ +"""Grader for the latte scene: milk poured into the coffee without emptying the pitcher or spilling. + +Rubric stages (weights and modes live in RUBRIC): + pitcher_grasped the pitcher taken — the scene's own auto-weld grasp contract engaged on the + pitcher's handle bar by either hand (`_auto_state` of `weld_pitcher` / + `weld_pitcher_l`); a milestone, credit kept after the release + transferred linear ramp of the scene's `transfer_fraction()` (milk particles inside the + mug) from 0 to `cfg.success_transfer_min` + poured the scene's own success: all four gates at once — milk transferred + (>= success_transfer_min), milk kept in the pitcher (>= success_kept_min), + coffee retained (>= success_retention_min), nothing spilled + (<= success_spilled_max). The three guards are sanity predicates on the done + rung (a live stage that is true from the first step would score a run that + did nothing), not weighted rungs of their own + plausible the VLM plausibility gate on the rendered final frame (final rung): two + upright vessels resting on the table, coffee with milk poured in — rejects + the pitcher inside/on the mug, tipped vessels, vessels held mid-air or off + the table, liquid pooled on the table +Every stage is per env (the MPM scene is single-env; scalars broadcast). Success = the scene's +`success()` on the final state AND the plausibility gate. + +Not measurable from state (hence the gate): a pitcher dunked into the mug passes every particle +metric; "upright and resting on the table" has no scene predicate, and inventing a tilt/height +tolerance is not allowed. +""" + +from __future__ import annotations + +from robobench.core import BaseGrader +from robobench.suites.deformable.scenes.latte import LatteScene + +GATE_PROMPT = ( + "Task: pour some milk from a small steel pitcher into a black ceramic mug of brown coffee " + "standing on a table, without emptying the pitcher and without spilling. Liquids are rendered " + "as dense colored particles (brown coffee, white milk). A plausible completed final scene shows " + "both vessels upright and resting on the table top, separate from each other, with the mug " + "containing coffee to which white milk has visibly been added. Reject if any of these is " + "visible: the pitcher is inside, on top of, or leaning against the mug; either vessel is tipped " + "over, lying on its side or upside down; either vessel is held in mid-air by a robot gripper or " + "is off the table; liquid is pooled on the table or floor; the mug shows no sign of milk. Answer " + "only about what is visible." +) + + +class LatteGrader(BaseGrader): + """Milk poured into the coffee mug — the scene's four gates met — with both vessels left upright on the table. + + Ladder: pitcher grasped 0.25 · milk transferred 0.50 · poured (4 gates) 0.75 · gate 1.00 + """ + + SCENE = LatteScene + RUBRIC = (("pitcher_grasped", 1, "once"), ("transferred", 1), ("poured", 1), ("plausible", 1, "final")) + # Final-frame camera (env-local meters): three-quarter view over the mug at (0, 0) and the + # pitcher at (0.16, 0) on the table top (z = 0.04), close enough to read the liquid surfaces. + # From the +y side, opposite the two Frankas (bases at y=-0.42): the camera at (0.5,-0.6,0.5) + # sat behind the pouring arm, which filled the frame (2026-09-05). Vessels in front, arms behind. + CAMERA_EYE = (0.10, 0.80, 0.65) + CAMERA_TARGET = (0.08, 0.0, 0.06) + scene: LatteScene + + def setup(self) -> None: + from .vlm_judge import open_capture + + self._pitcher_welds = [lbl for (_, vessel), lbl in self.scene.AUTO_PAIRS.items() if vessel == "pitcher"] + # Viewport capture for the final frame — attached now (right after the graded reset, + # when the render graph wires reliably); a failure is printed and retried at verdict. + self._capture = open_capture(self.env, "latte grader") + + def check_success(self): + return self.scene.success() + + # ---- rubric stages — each returns a (num_envs,) value in [0, 1] --------------------------- + def pitcher_grasped(self): + return float(any(self.scene._auto_state.get(lbl, False) for lbl in self._pitcher_welds)) + + def transferred(self): + return (self.scene.transfer_fraction() / self.scene.cfg.success_transfer_min).clamp(0.0, 1.0) + + def poured(self): + return self.scene.success().float() + + def plausible(self): + from .vlm_judge import gate_final_frames + + def prepare() -> None: + # The Kit render shows MPM liquids frozen at spawn unless their positions are pushed + # into Fabric (the scene's own workaround) — do it once before the final frame. + self.scene.setup_particle_visuals() + self.scene.push_particle_visuals() + + return gate_final_frames(self.env, self._capture, GATE_PROMPT, + self.CAMERA_EYE, self.CAMERA_TARGET, prepare=prepare) diff --git a/reference/robobench/suites/deformable/grader/shoe_knot.py b/reference/robobench/suites/deformable/grader/shoe_knot.py new file mode 100644 index 0000000000000000000000000000000000000000..abd327389be416991029f9a8ee1fef357d68ab99 --- /dev/null +++ b/reference/robobench/suites/deformable/grader/shoe_knot.py @@ -0,0 +1,159 @@ +"""Grader for the knot scene: a self-holding half knot tied from the two laces, seated on the tongue. + +Rubric stages (weights and modes live in RUBRIC): + wound linear ramp of the scene's `knot_winding()` (lace 1's winding around lace 2 on the + knot sections, degrees) from 0 to `cfg.winding_min_deg` + snug linear ramp of the scene's `knot_contacts()` (cross-lace contact pairs on the knot + sections) from 0 to `cfg.contacts_min` + tied the scene's own `success(world)`: winding >= winding_min_deg AND contacts >= + contacts_min AND the knot (closest cross-lace pair) seated at task-local + z < `cfg.knot_z_max`. The seat gate is a sanity predicate on this done rung, not a + rung of its own: with the laces at rest the closest cross-lace pair already sits + at z ~ 0.12 < 0.155, so a standalone height rung would score a run that did nothing + plausible the VLM plausibility gate on the rendered final frame (final rung): a single half + knot on the tongue, shoe upright and in place — rejects laces wrapped around the + shoe, shoe moved/flipped, laces merely crossed or lying loose, ends tied around + anything else +The scene's metrics are numpy, per world: every stage loops over the worlds and returns a +`(num_envs,)` tensor. Success = the scene's `success(world)` on the final state AND the +plausibility gate. + +Not measurable from state (hence the gate): the scene exposes no slack/hold check (the smoke's +check window is smoke logic, not a scene predicate) and no "knot is around lace 2, not the shoe" +test — winding + contacts are also satisfied by laces wound around the shoe body. +""" + +from __future__ import annotations + +from robobench.core import BaseGrader +from robobench.suites.deformable.scenes.shoe_knot import LACE_VISUAL_LINEAR, ShoeKnotScene + +GATE_PROMPT = ( + "Task: tie a single half knot from a sneaker's two separate shoelaces so that it sits on the " + "shoe's tongue, with the shoe left upright and in place on the gray table. The laces are rendered " + "as thin brown capsule chains. A plausible completed final scene shows the sneaker upright, " + "sole down, in the middle of the table, with the two laces intertwined in one knot resting on " + "the tongue and the free ends draping down the sides. Reject if any of these is visible: the " + "laces are wrapped around the shoe body or the sole instead of around each other; the shoe has " + "been moved, tipped, flipped or knocked off the table; the laces are merely crossed or lying " + "loose with no knot; a lace end is tied around anything other than the other lace (the shoe, the " + "table, itself); the laces are torn or floating in mid-air away from the shoe. Answer only about " + "what is visible." +) + + +class ShoeKnotGrader(BaseGrader): + """A self-holding half knot tied from the two laces and seated on the tongue — the scene's gates met — judged plausible. + + Ladder: wound 0.25 · snug 0.50 · tied (scene gates) 0.75 · gate 1.00 + """ + + SCENE = ShoeKnotScene + RUBRIC = (("wound", 1), ("snug", 1), ("tied", 1), ("plausible", 1, "final")) + # Final-frame camera (env-local meters): the knot smoke's recording view (eye 0.33 -0.31 0.40 / + # target 0.0 0.03 0.10 relative to the table top at z = 0.2), the whole shoe in frame. + CAMERA_EYE = (0.33, -0.31, 0.60) + CAMERA_TARGET = (0.0, 0.03, 0.30) + scene: ShoeKnotScene + + def setup(self) -> None: + from .vlm_judge import open_capture + + self._lace_visuals = None # authored lazily, for the final frame only + # Viewport capture for the final frame — attached now (right after the graded reset, + # when the render graph wires reliably); a failure is printed and retried at verdict. + self._capture = open_capture(self.env, "knot grader") + + def _worlds(self): + return range(self.num_envs) + + def check_success(self): + import torch + + return torch.tensor([bool(self.scene.success(w)) for w in self._worlds()]) + + # ---- rubric stages — each returns a (num_envs,) value in [0, 1] --------------------------- + def wound(self): + import torch + + c = self.scene.cfg + return torch.tensor([self.scene.knot_winding(w) / c.winding_min_deg for w in self._worlds()]).clamp(0.0, 1.0) + + def snug(self): + import torch + + c = self.scene.cfg + return torch.tensor([self.scene.knot_contacts(w)[0] / c.contacts_min for w in self._worlds()]).clamp(0.0, 1.0) + + def tied(self): + return self.check_success().float() + + def plausible(self): + from .vlm_judge import gate_final_frames + + return gate_final_frames(self.env, self._capture, GATE_PROMPT, + self.CAMERA_EYE, self.CAMERA_TARGET, prepare=self._sync_lace_visuals) + + # ---- render preparation ------------------------------------------------------------------ + def _sync_lace_visuals(self) -> None: + """The rods are hook-injected Newton bodies with NO USD prims (`is_visible=False`), so a + plain render shows a shoe with no dynamic laces. Author one visual capsule per rod + segment (plus the static permanent bridge arcs) under /World/GraderLaceVisuals — the + knot smoke's LaceVisuals recipe — and pose them from `body_q` before the final frame.""" + import numpy as np + import warp as wp + from pxr import Gf, Sdf, UsdGeom, UsdShade + + scene = self.scene + stage = self.env.stage + if self._lace_visuals is None: + c = scene.cfg + root = "/World/GraderLaceVisuals" + UsdGeom.Xform.Define(stage, root) + mat = UsdShade.Material.Define(stage, f"{root}/mat") + pbr = UsdShade.Shader.Define(stage, f"{root}/mat/pbr") + pbr.CreateIdAttr("UsdPreviewSurface") + pbr.CreateInput("diffuseColor", Sdf.ValueTypeNames.Color3f).Set(Gf.Vec3f(*LACE_VISUAL_LINEAR)) + pbr.CreateInput("roughness", Sdf.ValueTypeNames.Float).Set(0.85) + pbr.CreateInput("ior", Sdf.ValueTypeNames.Float).Set(1.0) # thin capsules: pure diffuse + pbr.CreateInput("metallic", Sdf.ValueTypeNames.Float).Set(0.0) + mat.CreateSurfaceOutput().ConnectToSource(pbr.ConnectableAPI(), "surface") + + def capsule(path: str, height: float): + cap = UsdGeom.Capsule.Define(stage, path) + cap.CreateAxisAttr(UsdGeom.Tokens.z) + cap.CreateRadiusAttr(float(c.rod_radius)) + cap.CreateHeightAttr(float(height)) + UsdShade.MaterialBindingAPI.Apply(cap.GetPrim()).Bind(mat) + return cap.AddTransformOp() + + def mat4(pos, quat_xyzw): + m = Gf.Matrix4d() + m.SetRotate(Gf.Quatd(float(quat_xyzw[3]), float(quat_xyzw[0]), + float(quat_xyzw[1]), float(quat_xyzw[2]))) + m.SetTranslateOnly(Gf.Vec3d(float(pos[0]), float(pos[1]), float(pos[2]))) + return m + + ops, bodies = [], [] + for w in self._worlds(): + R, t = scene.world_frames[w] + for k, arc in enumerate(scene.perm_arcs): # static bridges, posed once + arc_w = arc @ R.T + t + for j, (a, b) in enumerate(zip(arc_w[:-1], arc_w[1:])): + d = b - a + length = float(np.linalg.norm(d)) + q = wp.quat_between_vectors(wp.vec3(0.0, 0.0, 1.0), wp.vec3(*(d / length))) + op = capsule(f"{root}/w{w}_perm{k}_seg{j}", length) + op.Set(mat4(0.5 * (a + b), [q[0], q[1], q[2], q[3]])) + for i, lace_bodies in enumerate(scene.lace_bodies_w[w]): # dynamic segments + seg_lens = np.linalg.norm(np.diff(scene.lace_rest[i], axis=0), axis=1) + for j, body in enumerate(lace_bodies): + ops.append(capsule(f"{root}/w{w}_lace{i}_seg{j}", float(seg_lens[j]))) + bodies.append(int(body)) + self._lace_visuals = (ops, np.array(bodies), mat4) + print(f"[knot grader] authored {len(ops)} lace visual capsules under {root}", flush=True) + ops, bodies, mat4 = self._lace_visuals + q = scene._nm._state_0.body_q.numpy()[bodies] + with Sdf.ChangeBlock(): + for op, row in zip(ops, q): + op.Set(mat4(row[:3], row[3:7])) diff --git a/reference/robobench/suites/deformable/grader/tshirt.py b/reference/robobench/suites/deformable/grader/tshirt.py new file mode 100644 index 0000000000000000000000000000000000000000..9a431b19a83e793eb4c2e6e1eff45de7559c87b4 --- /dev/null +++ b/reference/robobench/suites/deformable/grader/tshirt.py @@ -0,0 +1,116 @@ +"""Grader for the tshirt scene: the shirt folded flat — sleeves to the center line, hem up over the collar. + +Rubric stages (weights and modes live in RUBRIC): + sleeves fraction of the two sleeve folds done — for each sleeve, the linear progress of + its tip particle (the max-x / min-x cloth particle at the graded reset) from its + reset x to the shirt's center line (x = 0, the table's axis); two identical + sub-goals, so ONE stage worth two rungs, value k/2 + hem the bottom hem's edge particle (max-y at reset) carried from its reset y to the + collar's reset y (min-y at reset): linear, 1 at or past the collar + folded the scene's own success: footprint (x-extent · y-extent of the cloth) at or + below `cfg.fold_footprint_max` + plausible the VLM plausibility gate on the rendered final frame (final rung): a neat flat + compact bundle on the table — rejects crumpled/balled cloth, cloth hanging off + the table, cloth still spread out +Every stage is per env. Success = the scene's `success()` (footprint gate) on the final state +AND the plausibility gate — the gate can only take credit away. + +Not measurable from state (hence the gate): whether the small footprint is a fold or a crumple, +and whether the cloth rests on the table rather than hanging off it — the scene has no such +predicates and inventing tolerances is not allowed. +""" + +from __future__ import annotations + +from robobench.core import BaseGrader +from robobench.suites.deformable.scenes.tshirt import TshirtFoldingScene + +GATE_PROMPT = ( + "Task: a red T-shirt lying flat on a gray box table was to be folded — both sleeves folded in " + "to the center line, then the bottom hem folded up over the collar. A plausible completed final " + "scene shows a single neat, flat, compact bundle of red cloth resting on the table top, roughly " + "rectangular with straight edges and no large loose flaps. Reject if any of these is visible: " + "the cloth is crumpled, bunched or balled up; any part of the cloth hangs over the table edge or " + "lies off the table; the shirt is still spread out or only partly folded (a sleeve or the hem " + "still extended); the cloth is held in the air by the robot gripper instead of resting on the " + "table. Answer only about what is visible." +) + + +class TshirtFoldingGrader(BaseGrader): + """The T-shirt folded into a flat compact bundle on the table (footprint gate), judged plausible. + + Ladder: 1 sleeve 0.20 · 2 sleeves 0.40 · hem over collar 0.60 · folded (footprint) 0.80 · gate 1.00 + """ + + SCENE = TshirtFoldingScene + # `sleeves` carries two identical sub-goals (left, right), one rung each -> weight 2. + RUBRIC = (("sleeves", 2), ("hem", 1), ("folded", 1), ("plausible", 1, "final")) + # Final-frame camera (env-local meters): high three-quarter view over the box table at + # (0, -0.5) whose top is z = 0.2, so the whole 0.8 x 0.8 m table and the cloth are in frame. + CAMERA_EYE = (0.9, -1.4, 1.2) + CAMERA_TARGET = (0.0, -0.5, 0.2) + scene: TshirtFoldingScene + + def setup(self) -> None: + from .vlm_judge import open_capture + + p0 = self.scene.nodal_pos_local() # (num_envs, P, 3) env-local, at the graded reset + # Landmark particles from the reset layout: sleeve tips are the x extremes, the hem the + # max-y edge (y ~ -0.18) and the collar the min-y edge (y ~ -0.83). + self._i_left = p0[..., 0].argmax(dim=1) + self._i_right = p0[..., 0].argmin(dim=1) + self._i_hem = p0[..., 1].argmax(dim=1) + i_collar = p0[..., 1].argmin(dim=1) + self._x_left0 = self._pick(p0, self._i_left, 0) + self._x_right0 = self._pick(p0, self._i_right, 0) + self._y_hem0 = self._pick(p0, self._i_hem, 1) + self._y_collar0 = self._pick(p0, i_collar, 1) + if not bool((self._x_left0 > 0).all() and (self._x_right0 < 0).all() + and (self._y_hem0 > self._y_collar0).all()): + raise RuntimeError("tshirt grader: reset layout does not straddle the center line " + f"(left x {self._x_left0.tolist()}, right x {self._x_right0.tolist()}, " + f"hem y {self._y_hem0.tolist()}, collar y {self._y_collar0.tolist()})") + # Viewport capture for the final frame — attached now (right after the graded reset, + # when the render graph wires reliably); a failure is printed and retried at verdict. + self._capture = open_capture(self.env, "tshirt grader") + + @staticmethod + def _pick(p, idx, axis: int): + """(num_envs,) coordinate `axis` of particle `idx[e]` in env e.""" + return p[..., axis].gather(1, idx.unsqueeze(1)).squeeze(1) + + def check_success(self): + return self.scene.success() + + # ---- rubric stages — each returns a (num_envs,) value in [0, 1] --------------------------- + # The sub-fold rungs nest under `folded` (as seated => aligned => grasped elsewhere): a + # shirt that passes the scene's footprint gate has necessarily brought its sleeves and hem + # in, whatever fold sequence it used, so the ladder always tops out at the official + # success (rule 6). The landmark ramps only shape the credit on the way there. + def sleeves(self): + import torch + + p = self.scene.nodal_pos_local() + x_left = self._pick(p, self._i_left, 0) + x_right = self._pick(p, self._i_right, 0) + left = ((self._x_left0 - x_left) / self._x_left0).clamp(0.0, 1.0) # +x tip -> x = 0 + right = ((x_right - self._x_right0) / -self._x_right0).clamp(0.0, 1.0) # -x tip -> x = 0 + return torch.maximum((left + right) / 2, self.scene.success().float()) + + def hem(self): + import torch + + p = self.scene.nodal_pos_local() + y_hem = self._pick(p, self._i_hem, 1) + ramp = ((self._y_hem0 - y_hem) / (self._y_hem0 - self._y_collar0)).clamp(0.0, 1.0) + return torch.maximum(ramp, self.scene.success().float()) + + def folded(self): + return self.scene.success().float() + + def plausible(self): + from .vlm_judge import gate_final_frames + + return gate_final_frames(self.env, self._capture, GATE_PROMPT, + self.CAMERA_EYE, self.CAMERA_TARGET) diff --git a/reference/robobench/suites/locomanip/grader/box_to_bin.py b/reference/robobench/suites/locomanip/grader/box_to_bin.py new file mode 100644 index 0000000000000000000000000000000000000000..3c589b22a5fde8c02217062df4d18dc9b898998f --- /dev/null +++ b/reference/robobench/suites/locomanip/grader/box_to_bin.py @@ -0,0 +1,52 @@ +"""Grader for the box-to-bin scene: the box taken off the shelf, walked around the corner, and left in the bin. + +Rubric stages (weights and modes live in RUBRIC) — the scene's own chain lifted -> transited -> +placed, one rung each: + picked the scene's `lifted` latch: the box got clear of its shelf board, either raised + `cfg.lift_h` above its resting height or seen held up in the apron in front of the + shelf face (both pick styles the open shelving affords); a milestone + carried the scene's own `carried_fraction()` — the fraction of the start-to-bin xy distance + the box has closed, a linear ramp — reading 1 once the scene's `transited` latch has + fired (its own "the transit is done" mark, which `success()` requires); a milestone, + so a box that arrives and is released keeps the credit + in_bin the scene's own `placed()` — fully inside the bin's inset interior bands, below the + rim-excluding height cap, and settled; live, so it dips if the box leaves the bin +Success is the scene's own `success()` — `lifted & transited & placed()` — read on the final +state; the three rungs are exactly its three conjuncts, so progress reads 1.0 iff it holds. +""" + +from __future__ import annotations + +from robobench.core import BaseGrader +from robobench.suites.locomanip.scenes.box_to_bin import BoxToBinScene + + +class BoxToBinGrader(BaseGrader): + """The cardboard box picked off the shelf, carried around the corner, and resting inside the sorting bin. + + Ladder: picked 0.333 · carried 0.667 (linear in the distance closed) · in bin 1.00 + """ + + SCENE = BoxToBinScene + RUBRIC = (("picked", 1, "once"), ("carried", 1, "once"), ("in_bin", 1)) + scene: BoxToBinScene + + def setup(self) -> None: + pass # every reference is the scene's own (latches, cfg bands); nothing to capture + + def check_success(self): + return self.scene.success() + + # ---- rubric stages — each returns a (num_envs,) value in [0, 1] ----------------------- + def picked(self): + return self.scene._lifted.float() + + def carried(self): + import torch + + s = self.scene + return torch.where(s._transited, torch.ones_like(s._transited, dtype=torch.float32), + s.carried_fraction().clamp(0.0, 1.0)) + + def in_bin(self): + return self.scene.placed().float() diff --git a/reference/robobench/suites/locomanip/grader/fruit_delivery.py b/reference/robobench/suites/locomanip/grader/fruit_delivery.py new file mode 100644 index 0000000000000000000000000000000000000000..4bca7062d298d4afe7c775b66c99a3530dff2602 --- /dev/null +++ b/reference/robobench/suites/locomanip/grader/fruit_delivery.py @@ -0,0 +1,86 @@ +"""Grader for the fruit delivery scene: every fruit carried around the table and set on the far plate. + +Rubric stages (weights and modes live in RUBRIC), each a fraction over the fruits PRESENT this +episode (the scene's `present` mask; identical fruits share one stage): + lifted fraction of fruits ever raised clear of the table — the fruit's origin (the produce + assets sit with their origin at the base, on `cfg.surface_z`) above the plate's rim + height (`cfg.plate_rim_z`), the height every fruit must clear to enter the dish; a + milestone, credit kept after the fruit is set down + carried mean transit progress: for each fruit, a linear ramp of its radial xy distance from + the plate's counted centre (the scene's own body-frame measure, see `_on_plate`) from + where it lay at the graded reset (0) to the counted footprint edge + `cfg.radius_frac * cfg.plate_radius` (1); a milestone, so a fruit that arrives and is + released keeps the credit + placed the scene's own `fruits_placed()` — on the plate AND settled — as a fraction of the + present fruits, GATED by the scene's `nonfruits_on_plate()` being empty: a pumpkin or + onion on the plate fails the task, so it zeroes the done stage rather than being a + weighted stage of its own (it is trivially "true" at reset) +Success is the scene's own `success()`: every present fruit placed and nothing that is not a fruit +on the plate, read on the final state. +""" + +from __future__ import annotations + +from robobench.core import BaseGrader +from robobench.suites.locomanip.scenes.fruit_delivery import FruitDeliveryScene + + +class FruitDeliveryGrader(BaseGrader): + """Every fruit carried around the table and resting on the far-side plate, nothing else put on it. + + Ladder (one fruit): lifted 0.333 · carried 0.667 · placed 1.00 + With N fruits every rung is the fraction over the N present fruits, so k fruits fully + delivered score k/N. + """ + + SCENE = FruitDeliveryScene + RUBRIC = (("lifted", 1, "once"), ("carried", 1, "once"), ("placed", 1)) + scene: FruitDeliveryScene + + def setup(self) -> None: + s = self.scene + # each fruit's radial distance from the plate's counted centre at the graded reset — + # the start of its transit ramp + self._r0 = self._radial()[:, s._fruit_idx].clone() # (num_envs, n_fruits) + + def check_success(self): + return self.scene.success() + + def _radial(self): + """(num_envs, n_items) radial xy distance of every item from the plate's GEOMETRIC centre, + in the plate's body frame — the same measure the scene's `_on_plate` footprint test uses.""" + import torch + from isaaclab.utils.math import quat_apply_inverse + + s = self.scene + pp, pq = s.plate.data.root_pos_w, s.plate.data.root_quat_w + cols = [] + for name in s.names: + loc = quat_apply_inverse(pq, s.items[name].data.root_pos_w - pp) + cols.append((loc[:, :2] - s._plate_center).norm(dim=-1)) + return torch.stack(cols, dim=1) + + def _frac_present(self, per_fruit): + """Mean of a per-fruit (num_envs, n_fruits) value over the fruits present in each env.""" + pres = self.scene.fruits_present() + return (per_fruit.float() * pres).sum(dim=1) / pres.sum(dim=1).clamp(min=1).float() + + # ---- rubric stages — each returns a (num_envs,) value in [0, 1] ----------------------- + def lifted(self): + import torch + + s = self.scene + z = torch.stack([s.items[n].data.root_pos_w[:, 2] for n in s.names], dim=1)[:, s._fruit_idx] + z = z - s.env_origins[:, 2:3] + return self._frac_present(z > s.cfg.surface_z + s.cfg.plate_rim_z) + + def carried(self): + s = self.scene + r = self._radial()[:, s._fruit_idx] + span = (self._r0 - s._plate_r).clamp(min=1e-6) # start -> counted footprint edge + return self._frac_present(((self._r0 - r) / span).clamp(0.0, 1.0)) + + def placed(self): + s = self.scene + clean = ~s.nonfruits_on_plate().any(dim=1) + return self._frac_present(s.fruits_placed()) * clean.float() diff --git a/reference/robobench/suites/locomanip/grader/wheel_carry.py b/reference/robobench/suites/locomanip/grader/wheel_carry.py new file mode 100644 index 0000000000000000000000000000000000000000..58bd0790ba9cba3549ef112ce188e67b274852bd --- /dev/null +++ b/reference/robobench/suites/locomanip/grader/wheel_carry.py @@ -0,0 +1,52 @@ +"""Grader for the wheel carry scene: the wheel picked up here, walked over there, and left in the basket. + +Rubric stages (weights and modes live in RUBRIC) — the scene's own chain lifted -> transited -> +placed, one rung each: + picked the scene's `lifted` latch: the wheel's origin raised `cfg.lift_h` above the pick + table top; a milestone, credit kept after it is put down + carried the scene's own `carried_fraction()` — the fraction of `cfg.carry_dx` the wheel + has travelled along +x from the pick station, a linear ramp — reading 1 once the + scene's `transited` latch has fired (its own "the transit is done" mark, which + `success()` requires); a milestone, so a wheel that arrives and is released keeps + the credit + in_basket the scene's own `placed()` — inside the basket's target bands, below the height + cap, and settled; live, so it dips if the wheel leaves the basket +Success is the scene's own `success()` — `lifted & transited & placed()` — read on the final +state; the three rungs are exactly its three conjuncts, so progress reads 1.0 iff it holds. +""" + +from __future__ import annotations + +from robobench.core import BaseGrader +from robobench.suites.locomanip.scenes.wheel_carry import WheelCarryScene + + +class WheelCarryGrader(BaseGrader): + """The steering wheel lifted off the near table, carried across, and resting inside the far basket. + + Ladder: picked 0.333 · carried 0.667 (linear in the distance closed) · in basket 1.00 + """ + + SCENE = WheelCarryScene + RUBRIC = (("picked", 1, "once"), ("carried", 1, "once"), ("in_basket", 1)) + scene: WheelCarryScene + + def setup(self) -> None: + pass # every reference is the scene's own (latches, cfg bands); nothing to capture + + def check_success(self): + return self.scene.success() + + # ---- rubric stages — each returns a (num_envs,) value in [0, 1] ----------------------- + def picked(self): + return self.scene._lifted.float() + + def carried(self): + import torch + + s = self.scene + return torch.where(s._transited, torch.ones_like(s._transited, dtype=torch.float32), + s.carried_fraction().clamp(0.0, 1.0)) + + def in_basket(self): + return self.scene.placed().float() diff --git a/reference/robobench/suites/packing/grader/clear_organic_objects.py b/reference/robobench/suites/packing/grader/clear_organic_objects.py new file mode 100644 index 0000000000000000000000000000000000000000..6d56168e1b67d6a243d9de9963da758a8be60877 --- /dev/null +++ b/reference/robobench/suites/packing/grader/clear_organic_objects.py @@ -0,0 +1,94 @@ +"""Grader for the clear_organic_objects scene: every organic in the bin, nothing else in it. + +Rubric stages (weights and modes live in RUBRIC): + organics_cleared fraction of the required placements that hold right now — in the + legacy variant every PRESENT organic cleared into the bin (the scene's + `cleared()`: origin inside the bin's interior box in the bin's body + frame, settled); in the sort variant (`sort_to_bin` / `sort_to_tray`) + the scene's own `bin_targets` cleared plus its `tray_targets` resting + on the tray. The organics are interchangeable sub-goals, so they share + this one fraction stage. It is GATED by the two sanity predicates + below: the whole stage reads 0 while either fails, and recovers when + the state is repaired (live) +Gates (no weight of their own — rule 7 sanity predicates on the one and only stage, which +is also the done stage): + no distractor in the bin the scene's `distractors_in_bin()` — identification IS the + task: dumping everything in must not score as progress + bin upright the bin's up-axis within its own topple angle of world-up, the + angle (from `bin_bbox` x `bin_scale`) past which a free crate's + centre leaves its base and it falls onto its side. A knocked- + over bin with produce inside it must not pass. The default bin + is kinematic and cannot move, so this only bites in the + dynamic-bin variant +Success is the scene's own `success()` (every required placement holds, no distractor in the +bin) AND the bin upright, on the final state. `progress == 1` exactly when that holds. + +Not measured: a grasped/lifted rung per item (the scene exposes no lift height or hold +criterion) and "bin in place" (a positional test would need an invented tolerance; the +body-frame judging already makes a moved bin judge identically). +""" + +from __future__ import annotations + +import math + +from robobench.core import BaseGrader +from robobench.suites.packing.scenes.clear_organic_objects import ClearOrganicObjectsScene + + +class ClearOrganicObjectsGrader(BaseGrader): + """Every organic item in the bin, no non-food item in it, the bin still upright. + + Ladder (N required placements): k placed k / N — e.g. the default manifest's 15 organics: + 1 in 0.067 · 2 in 0.133 · ... · 14 in 0.933 · 15 in 1.00 (a preset with 11 present: 0.091 + per organic); any non-food item in the bin, or the bin tipped over, gates the stage to 0 + until repaired. + """ + + SCENE = ClearOrganicObjectsScene + # One stage: the sub-goals are identical, and "all cleared" is not a separate physical + # action (unlike a lid or a set-down), so there is no distinct done step to weight. + RUBRIC = (("organics_cleared", 1.0),) + scene: ClearOrganicObjectsScene + + def setup(self) -> None: + c = self.scene.cfg + # Topple angle of the (scaled) crate: centre at half height, pivot on the shorter + # base edge — tan(theta) = half_short_side / (height / 2). + half_short = min(c.bin_bbox[0] * c.bin_scale[0], c.bin_bbox[1] * c.bin_scale[1]) / 2 + height = c.bin_bbox[2] * c.bin_scale[2] + self._upright_cos = math.cos(math.atan2(half_short, height / 2)) + + def check_success(self): + return self.scene.success() & self._bin_upright() + + def _bin_upright(self): + """(num_envs,) bool: the bin's body +z within its topple angle of world-up. + R[2,2] of a (w, x, y, z) quaternion is 1 - 2 (x^2 + y^2).""" + q = self.scene.bin.data.root_quat_w + up_z = 1.0 - 2.0 * (q[:, 1] ** 2 + q[:, 2] ** 2) + return up_z >= self._upright_cos + + def _sane(self): + """(num_envs,) bool: no distractor in the bin AND the bin upright.""" + return ~self.scene.distractors_in_bin().any(dim=1) & self._bin_upright() + + # ---- rubric stages — each returns a (num_envs,) value in [0, 1] --------------------- + def organics_cleared(self): + import torch + + s = self.scene + if s.tray_targets or s.cfg.sort_to_bin: + # sort variant: the scene's own required placements, judged live (its latched + # flags are the scene's partial-credit device; the grader keeps state live) + idx = {n: i for i, n in enumerate(s.names)} + cleared = s.cleared() + on_tray = s.placed_on_tray() + cols = [cleared[:, idx[n]] for n in s.bin_targets] + cols += [on_tray[:, idx[n]] for n in s.tray_targets] + frac = torch.stack(cols, dim=1).float().mean(dim=1) + else: + pres = s.organics_present() # (n, O) + done = s.organics_cleared() & pres + frac = done.sum(dim=1).float() / pres.sum(dim=1).clamp(min=1).float() + return frac * self._sane().float() diff --git a/reference/robobench/suites/packing/grader/egg_carton.py b/reference/robobench/suites/packing/grader/egg_carton.py new file mode 100644 index 0000000000000000000000000000000000000000..151ca8ba6e58d0b98c3679b6fd2af9712a60f670 --- /dev/null +++ b/reference/robobench/suites/packing/grader/egg_carton.py @@ -0,0 +1,55 @@ +"""Grader for the egg_carton scene: `target_eggs` distinct pockets filled with upright eggs, lid closed. + +Rubric stages (weights and modes live in RUBRIC): + eggs_seated fraction of the `target_eggs` pockets occupied by a seated egg — the + scene's own `occupied()`: egg centre within `seat_xy_tol` of a cavity + centre in the carton frame, in the `seat_z_min..seat_z_max` band, long axis + within `egg_tilt_max_deg` of the pocket axis, still. Distinct pockets, so a + second egg dropped into an occupied pocket earns nothing; the eggs are + identical, so they share this one fraction stage + lid_closed the scene's own `success()`: every target pocket filled, the lid within + `lid_closed_deg` of shut (when `require_lid_closed`), eggs and lid settled. + Gated on the pockets being filled: shutting the lid on an empty carton is not + progress (it has to be reopened), and the scene's own rubric only awards the + lid after the eggs +Both stages are live: an egg that topples or a lid that swings back open loses its credit. +Success is the scene's own `success()` on the final state. + +Not measured: a grasped/lifted rung for the eggs. The scene exposes no lift height or hold +criterion, and inventing one is forbidden, so that rung is dropped. +""" + +from __future__ import annotations + +from robobench.core import BaseGrader +from robobench.suites.packing.scenes.egg_carton import EggCartonScene + + +class EggCartonGrader(BaseGrader): + """Three eggs seated upright in three distinct carton pockets, then the lid pushed closed. + + Ladder: 1 egg 0.30 · 2 eggs 0.60 · 3 eggs 0.90 · lid closed 1.00 + (0.90 * k / target_eggs per k pockets filled; the default target is three.) + """ + + SCENE = EggCartonScene + # 0.9 : 0.1 — the three identical eggs share one fraction stage (0.30 each); the lid push + # is one short non-prehensile action after three pick-and-place cycles, so it is worth + # less than an egg. + RUBRIC = (("eggs_seated", 0.9), ("lid_closed", 0.1)) + scene: EggCartonScene + + def setup(self) -> None: + pass # every quantity is read live from the scene's own predicates + + def check_success(self): + return self.scene.success() # (num_envs,) pockets filled, lid closed, settled + + # ---- rubric stages — each returns a (num_envs,) value in [0, 1] --------------------- + def eggs_seated(self): + target = self.scene.cfg.target_eggs + filled = self.scene.occupied().sum(dim=1).clamp(max=target) # (n,) distinct pockets + return filled.float() / float(target) + + def lid_closed(self): + return self.scene.success().float() diff --git a/reference/robobench/suites/packing/grader/pen_holder.py b/reference/robobench/suites/packing/grader/pen_holder.py new file mode 100644 index 0000000000000000000000000000000000000000..852fbc59629fea78848d904e1aaab2b03721a500 --- /dev/null +++ b/reference/robobench/suites/packing/grader/pen_holder.py @@ -0,0 +1,57 @@ +"""Grader for the pen_holder scene: every present pen in the holder tip-up, holder set down upright. + +Rubric stages (weights and modes live in RUBRIC): + pens_in fraction of the PRESENT pens the scene counts as in the holder — the + scene's own `counted()`: bottom within `xy_tol` of the holder axis, + deeper than `depth_min` below the rim, tip-up within `pen_align_max_deg`, + holder within `holder_tilt_max_deg` of up, pen and holder settled. All + pens are one identical family, so they share this one fraction stage; + the fraction is over the sampled subset (the present pen count is drawn + per env at reset, k ~ U{min_present..n} per family) + holder_placed the loaded holder standing upright on the work surface (the scene's + `holder_placed()`: within `placed_tilt_deg`, bottom within `placed_z_tol` + of the surface, still), gated on every present pen being in. The holder + spawns upright on the surface, so ungated it would be free credit at + reset; the order is fill, then set down +Both stages are live: a pen ejected or a holder knocked over loses its credit. Success is +the scene's own `success()` — `all_inserted() & holder_placed()` — on the final state. + +Not measured: a grasped/lifted rung for the pens. The scene exposes no lift height or hold +criterion, and inventing one is forbidden, so that rung is dropped. +""" + +from __future__ import annotations + +from robobench.core import BaseGrader +from robobench.suites.packing.scenes.pen_holder import PenHolderScene + + +class PenHolderGrader(BaseGrader): + """Every present pen in the holder tip-up, and the loaded holder standing upright on the surface. + + Ladder (N present pens): k pens in 0.90 * k / N · holder set down 1.00 + N = 4: 1 pen 0.225 · 2 pens 0.45 · 3 pens 0.675 · 4 pens 0.90 · set down 1.00 + N = 3: 1 pen 0.30 · 2 pens 0.60 · 3 pens 0.90 · set down 1.00 + """ + + SCENE = PenHolderScene + # 0.9 : 0.1 — the identical pens share one fraction stage; setting the filled holder + # down is one short action after up to four insertions, so like the egg carton's lid + # it is worth less than a pen. + RUBRIC = (("pens_in", 0.9), ("holder_placed", 0.1)) + scene: PenHolderScene + + def setup(self) -> None: + pass # every quantity is read live from the scene's own predicates + + def check_success(self): + return self.scene.success() # (num_envs,) all present pens in AND holder set down + + # ---- rubric stages — each returns a (num_envs,) value in [0, 1] --------------------- + def pens_in(self): + counted = self.scene.counted() # (n, P) inserted & settled & present + n_present = self.scene.present.sum(dim=1).clamp(min=1) + return counted.sum(dim=1).float() / n_present.float() + + def holder_placed(self): + return (self.scene.all_inserted() & self.scene.holder_placed()).float() diff --git a/reference/robobench/suites/packing/grader/tool_packing.py b/reference/robobench/suites/packing/grader/tool_packing.py new file mode 100644 index 0000000000000000000000000000000000000000..4510ba45767fe71c8973cb8fad539f875928cb2a --- /dev/null +++ b/reference/robobench/suites/packing/grader/tool_packing.py @@ -0,0 +1,57 @@ +"""Grader for the tool_packing scene: three tools stowed in their assigned drawers, cabinet shut. + +Rubric stages (weights and modes live in RUBRIC): + stowed fraction of the three tools inside their ASSIGNED drawer's tray — the + scene's own `stowed()`: origin inside the tray-interior box in the drawer's + body frame (wherever the drawer slid to), settled + drawer_shut fraction of the three tools that are stowed AND whose own drawer is shut + (joint within `drawer_closed_tol` of 0) — the second rung of each tool's + ladder, since every tool has its own drawer to push home + cabinet_shut the scene's own `success()`: all three stowed, EVERY drawer shut (the + distractor drawer included), both doors shut on the toolbox variant, and + items + joints settled +All three stages are live: a tool knocked out of its tray or a drawer that slides back open +loses its credit. Success is the scene's own `success()` on the final state. + +Not measured: a grasped/lifted rung for the tools. The scene exposes no lift height or +hold criterion, and inventing one is forbidden, so that rung is dropped. +""" + +from __future__ import annotations + +from robobench.core import BaseGrader +from robobench.suites.packing.scenes.tool_packing import ToolPackingScene + + +class ToolPackingGrader(BaseGrader): + """All three tools stowed in their assigned drawers and the cabinet fully shut. + + Ladder: 1 stowed 0.15 · 2 stowed 0.30 · 3 stowed 0.45 · 1 drawer shut 0.60 · + 2 drawers shut 0.75 · 3 drawers shut 0.90 · cabinet shut 1.00 + """ + + SCENE = ToolPackingScene + # The three identical stows share one fraction stage (0.15 each) and the three identical + # drawer-shuts another (0.15 each); the final cabinet shut is one short closing step after + # the parts (on the chest it only adds the untouched distractor drawer + settle; on the + # toolbox, both doors), so like the egg carton's lid it is worth less than a part. + RUBRIC = (("stowed", 0.45), ("drawer_shut", 0.45), ("cabinet_shut", 0.10)) + scene: ToolPackingScene + + def setup(self) -> None: + pass # every quantity is read live from the scene's own predicates + + def check_success(self): + return self.scene.success() # (num_envs,) stowed, every drawer + door shut, settled + + # ---- rubric stages — each returns a (num_envs,) value in [0, 1] --------------------- + def stowed(self): + return self.scene.stowed().float().mean(dim=1) + + def drawer_shut(self): + stowed = self.scene.stowed() # (n, I) + own_closed = self.scene.drawers_closed()[:, self.scene._assigned] # (n, I) — privileged + return (stowed & own_closed).float().mean(dim=1) + + def cabinet_shut(self): + return self.scene.success().float() diff --git a/reference/robobench/suites/puzzle/grader/classify_objects.py b/reference/robobench/suites/puzzle/grader/classify_objects.py new file mode 100644 index 0000000000000000000000000000000000000000..a70d2b4d68ccf3305e421b24b23460d25e4bfc35 --- /dev/null +++ b/reference/robobench/suites/puzzle/grader/classify_objects.py @@ -0,0 +1,40 @@ +"""Grader for the classify_objects scene: every coloured block sorted into its matching zone. + +Rubric stages (weights and modes live in RUBRIC): + sorted_blocks fraction of the env's blocks correctly sorted — the scene's own + `sorted_mask()`: centre within cfg.zone_half (xy) of ITS OWN category's + (jittered) zone centre, resting on the surface (cfg.z_tol) and settled + (cfg.settle_speed) +The blocks are identical pick-and-place sub-goals, so they share this ONE fraction stage +(k/B). It is LIVE: a block knocked off its zone loses the credit, which is what makes the +final state the only one that counts. No grasp/lift rung: the scene defines no lift +threshold, so it is dropped rather than invented. Success is the scene's own `success()` +— every block sorted — read on the final state. +""" + +from __future__ import annotations + +from robobench.core import BaseGrader +from robobench.suites.puzzle.scenes.classify_objects import ClassifyObjectsScene + + +class ClassifyObjectsGrader(BaseGrader): + """Every block resting, settled, inside the zone of its own colour. + + Ladder (k / B blocks sorted; B = len(cfg.manifest), 3 by default): 1 block 0.333 · + 2 blocks 0.667 · 3 blocks 1.00. + """ + + SCENE = ClassifyObjectsScene + RUBRIC = (("sorted_blocks", 1),) + scene: ClassifyObjectsScene + + def setup(self) -> None: + pass # zones are captured by the scene itself at reset (`_zones`); nothing to cache + + def check_success(self): + return self.scene.success() # (num_envs,) — every block of that env sorted + + # ---- rubric stages — each returns a (num_envs,) fraction over that env's blocks -------- + def sorted_blocks(self): + return self.scene.n_sorted().float() / len(self.scene.cfg.manifest) diff --git a/reference/robobench/suites/puzzle/grader/coffee_service.py b/reference/robobench/suites/puzzle/grader/coffee_service.py new file mode 100644 index 0000000000000000000000000000000000000000..2397bb89093723de2895c342d2a9c992a6265a97 --- /dev/null +++ b/reference/robobench/suites/puzzle/grader/coffee_service.py @@ -0,0 +1,77 @@ +"""Grader for the coffee scene: one capsule coffee brewed and the filled mug set back on the tray. + +Rubric stages (weights and modes live in RUBRIC) — the scene's own seven staged flags +(`CoffeeServiceScene.STAGES`), in the order the appliance's rules impose: + pod_loaded the capsule seated in the bay pocket (`pod_seated`) + bay_closed the cover slid shut over the seated capsule (`pod_seated & cover_closed`) + cup_staged the mug standing on the platform under the spout (`cup_under_spout`) + started the machine accepted START — it only does with cover closed, pod seated and + cup staged (the `started` event of the appliance state machine) + brewed the brew ran to completion: the machine's latched `filled` flag (an abort on + early open or on a spill never sets it) + cup_out the filled mug taken off the platform (`filled & ~cup_under_spout`) + served the filled mug resting upright, settled, on the tray dish — the scene's own + `success()` +The first six are milestones (a loaded pod, a closed bay or a staged cup need not persist once +the brew is done and the mug is carried away); the scene latches them itself at substep rate, +so the grader reads its flags. `brewed` is the machine's irreversible state, `served` is the +live end state. Success is the scene's `success()` on the final state. +""" + +from __future__ import annotations + +from robobench.core import BaseGrader +from robobench.suites.puzzle.scenes.coffee_service import CoffeeServiceScene + + +class CoffeeServiceGrader(BaseGrader): + """The mug filled by a completed brew and resting upright, settled, on the serving tray. + + Ladder: pod loaded 0.143 · bay closed 0.286 · cup staged 0.429 · started 0.571 · + brewed 0.714 · cup out 0.857 · served 1.000 + (uniform: one unit per stage of the appliance's chain, the scene's own even credit). + """ + + SCENE = CoffeeServiceScene + RUBRIC = ( + ("pod_loaded", 1, "once"), + ("bay_closed", 1, "once"), + ("cup_staged", 1, "once"), + ("started", 1, "once"), + ("brewed", 1), + ("cup_out", 1, "once"), + ("served", 1), + ) + scene: CoffeeServiceScene + + def setup(self) -> None: + # the flag columns, by the scene's own stage names + self._col = {name: k for k, name in enumerate(self.scene.STAGES)} + + def check_success(self): + return self.scene.success() # filled AND on the tray, upright AND settled — live + + def _flag(self, name: str): + return self.scene.stage_flags()[:, self._col[name]].float() + + # ---- rubric stages — each returns a (num_envs,) value in [0, 1] -------------------------- + def pod_loaded(self): + return self._flag("pod_loaded") + + def bay_closed(self): + return self._flag("bay_closed") + + def cup_staged(self): + return self._flag("cup_staged") + + def started(self): + return self._flag("started") + + def brewed(self): + return self.scene.filled().float() + + def cup_out(self): + return self._flag("cup_out") + + def served(self): + return self.scene.success().float() diff --git a/reference/robobench/suites/puzzle/grader/push_shapes.py b/reference/robobench/suites/puzzle/grader/push_shapes.py new file mode 100644 index 0000000000000000000000000000000000000000..25c49eec7e7c6c8c495b9bf024eaf045a4d374f0 --- /dev/null +++ b/reference/robobench/suites/puzzle/grader/push_shapes.py @@ -0,0 +1,62 @@ +"""Grader for the push_shapes scene: the T, X and L blocks each pushed into their own cutout. + +Rubric stages (weights and modes live in RUBRIC). The three blocks are identical sub-goals, +so every stage is ONE fraction over the env's three blocks (k/3), never a stage per block: + yaw_aligned blocks whose orientation is inside the scene's coarse yaw band of their pad + (`yaw_matched`: cfg.yaw_stage_tolerance_deg) — the pivot push is done + near_pad blocks whose centre is within cfg.near_xy_tolerance of their own pad centre — + the translation push has arrived at the cutout + seated blocks seated in their recess AND settled — the scene's own per-piece success + gate (`seated() & settled()`: xy within cfg.xy_tolerance, orientation within + cfg.orientation_tolerance_deg, not lifted, dropped into the recess, at rest) +All three are LIVE: a block knocked back out of tolerance loses the credit, exactly as the +scene's own `success()` is a live conjunction. Every rung is implied by the next (7 mm inside +30 mm, 7 deg inside 10 deg), so a seated block earns all three. Success is the scene's own +`success()` — all three blocks seated and settled — read on the final state. +""" + +from __future__ import annotations + +from robobench.core import BaseGrader +from robobench.suites.puzzle.scenes.push_shapes import PIECES, PushShapesScene + + +class PushShapesGrader(BaseGrader): + """All three blocks pushed into their own cutouts — aligned, dropped in, and at rest. + + Ladder (3 blocks, 3 equal rungs each, 1/9 per rung): per block yaw aligned +1/9 · + near its pad +1/9 · seated +1/9; 1 block seated 0.333 · 2 blocks seated 0.667 · + 3 blocks seated 1.00. + """ + + SCENE = PushShapesScene + # Equal rungs: each is a distinct physical event the scene measures with its own band + # (yaw_stage_tolerance_deg, near_xy_tolerance, seated()); each stage is a fraction over + # the three blocks, so one block contributes 1/3 of each rung. + RUBRIC = (("yaw_aligned", 1), ("near_pad", 1), ("seated", 1)) + scene: PushShapesScene + + def setup(self) -> None: + pass # everything is measured against the pads, which never move after reset + + def check_success(self): + return self.scene.success() # (num_envs,) — all three seated AND settled, live + + # ---- rubric stages — each returns a (num_envs,) fraction over that env's blocks -------- + def yaw_aligned(self): + import torch + + return torch.stack([self.scene.yaw_matched(k) for k in PIECES], dim=1).float().mean(dim=1) + + def near_pad(self): + import torch + + tol = self.scene.cfg.near_xy_tolerance + near = torch.stack([self.scene.position_error(k) <= tol for k in PIECES], dim=1) + return near.float().mean(dim=1) + + def seated(self): + import torch + + done = torch.stack([self.scene.seated(k) & self.scene.settled(k) for k in PIECES], dim=1) + return done.float().mean(dim=1) diff --git a/reference/robobench/suites/puzzle/grader/spatula_flip_serve.py b/reference/robobench/suites/puzzle/grader/spatula_flip_serve.py new file mode 100644 index 0000000000000000000000000000000000000000..704b62aec0e8bbb895636f84b3f9a3a2e9f44d5f --- /dev/null +++ b/reference/robobench/suites/puzzle/grader/spatula_flip_serve.py @@ -0,0 +1,83 @@ +"""Grader for the spatula scene: the bread flipped in the pan and/or served onto the plate on the blade. + +The scene's `cfg.goal` picks the chain (its own `_stage_table`); the rubric follows the goal, +uniform credit per rung: + serve lifted -> wedged -> loaded -> served + flip lifted -> wedged -> flipped_in_pan + flip_serve lifted -> wedged -> flipped -> loaded (after the flip) -> served + +Rubric stages (weights and modes live in RUBRIC) — the scene's latched stages, read from its +substep-rate latches (the journey), plus the live destination: + lifted the blade above the surface by `lift_gate` (`_lifted`) — a milestone + wedged the bread riding the blade while still down at pan-floor level (`_wedged`) + flipped the bread inverted by >= `flip_min_deg` AND at rest flat in the pan + (`_flipped`); a milestone in `flip_serve` (the slice is served afterwards) + loaded the bread riding the blade above `lift_gate` — the friction carry + (`_loaded`; in `flip_serve` the scene's `_loaded_pf`, the carry AFTER the + flip, so a load during the flip lift earns nothing) + served the scene's `success()` for the serve goals: the bread resting flat on the + plate now, having ARRIVED ON THE BLADE (`_served`, the contact-history + clause; `flip_serve` also demands the flip latch) + flipped_in_pan the scene's `success()` for the flip goal: flipped latched AND the bread + resting inverted, flat and settled in the pan now +Success is the scene's own goal-dependent `success()` on the final state. +""" + +from __future__ import annotations + +from robobench.core import BaseGrader +from robobench.suites.puzzle.scenes.spatula_flip_serve import SpatulaFlipServeScene + + +class SpatulaFlipServeGrader(BaseGrader): + """The bread flipped in the pan and/or served flat onto the plate (per the scene's goal), carried there on the blade. + + Ladder (uniform, one unit per rung of the goal's chain): + flip_serve: lifted 0.20 · wedged 0.40 · flipped 0.60 · loaded after flip 0.80 · served 1.00 + serve: lifted 0.25 · wedged 0.50 · loaded 0.75 · served 1.00 + flip: lifted 0.333 · wedged 0.667 · flipped in pan 1.000 + """ + + SCENE = SpatulaFlipServeScene + RUBRICS = { + "serve": (("lifted", 1, "once"), ("wedged", 1, "once"), ("loaded", 1, "once"), + ("served", 1)), + "flip": (("lifted", 1, "once"), ("wedged", 1, "once"), ("flipped_in_pan", 1)), + "flip_serve": (("lifted", 1, "once"), ("wedged", 1, "once"), ("flipped", 1, "once"), + ("loaded", 1, "once"), ("served", 1)), + } + RUBRIC = RUBRICS["flip_serve"] # the scene's default goal + scene: SpatulaFlipServeScene + + def __init__(self, env) -> None: + # the chain is the scene's goal: pick that goal's rubric before the base wires the stages + scene = getattr(env, "scene", None) + if isinstance(scene, self.SCENE): + self.RUBRIC = self.RUBRICS[scene.cfg.goal] + super().__init__(env) + + def setup(self) -> None: + self._goal = self.scene.cfg.goal + + def check_success(self): + return self.scene.success() # goal-dependent: the journey latches AND the live destination + + # ---- rubric stages — each returns a (num_envs,) value in [0, 1] -------------------------- + def lifted(self): + return self.scene._lifted.float() + + def wedged(self): + return self.scene._wedged.float() + + def flipped(self): + return self.scene._flipped.float() + + def loaded(self): + latch = self.scene._loaded_pf if self._goal == "flip_serve" else self.scene._loaded + return latch.float() + + def served(self): + return self.scene.success().float() + + def flipped_in_pan(self): + return self.scene.success().float() diff --git a/reference/robobench/suites/puzzle/grader/stack_blocks.py b/reference/robobench/suites/puzzle/grader/stack_blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..1e50b19695772dcfabad34959c44ee9f8509ba95 --- /dev/null +++ b/reference/robobench/suites/puzzle/grader/stack_blocks.py @@ -0,0 +1,42 @@ +"""Grader for the stack_blocks scene: all blocks stacked into one upright tower on the pad. + +Rubric stages (weights and modes live in RUBRIC): + tower contiguous tower height in blocks over cfg.n_blocks — the scene's own + `tower_height()`: slots 0, 1, 2, ... above the pad centre, each filled by a block + within cfg.align_tol of the pad axis, at the slot height within cfg.z_tol, + upright within cfg.upright_max_deg, settled with the whole scene settled + (cfg.settle_speed), counted up to the first empty slot +The cubes are identical sub-goals, so they share this ONE fraction stage (k/n_blocks). It is +LIVE: a toppled tower loses the credit. No grasp/lift rung: the scene defines no lift +threshold, so it is dropped rather than invented. Success is the scene's own `success()` — +every slot filled by exactly one block — read on the final state. With n_blocks blocks and +n_blocks slots, a full-height tower (every slot >= 1 block) is exactly one block per slot, +so the stage reaches 1 if and only if `success()` holds. +""" + +from __future__ import annotations + +from robobench.core import BaseGrader +from robobench.suites.puzzle.scenes.stack_blocks import StackBlocksScene + + +class StackBlocksGrader(BaseGrader): + """One clean tower on the pad — every slot filled by exactly one upright, settled block. + + Ladder (k / n_blocks tower height; cfg.n_blocks = 4 by default): 1 block 0.25 · + 2 blocks 0.50 · 3 blocks 0.75 · 4 blocks 1.00. + """ + + SCENE = StackBlocksScene + RUBRIC = (("tower", 1),) + scene: StackBlocksScene + + def setup(self) -> None: + pass # the pad axis is cfg.pad_pos (env-local) and the slots are cfg-derived; nothing to cache + + def check_success(self): + return self.scene.success() # (num_envs,) — every slot of that env holds exactly one block + + # ---- rubric stages — each returns a (num_envs,) value in [0, 1] ------------------------- + def tower(self): + return self.scene.tower_height().float() / self.scene.cfg.n_blocks diff --git a/reference/robobench/suites/puzzle/grader/syringe_dosing.py b/reference/robobench/suites/puzzle/grader/syringe_dosing.py new file mode 100644 index 0000000000000000000000000000000000000000..e599a1f9b445370e5c44471271d515c21e777c83 --- /dev/null +++ b/reference/robobench/suites/puzzle/grader/syringe_dosing.py @@ -0,0 +1,60 @@ +"""Grader for the syringe scene: a full draw, one in-band dose in each of three tubes, syringe laid back down. + +Rubric stages (weights and modes live in RUBRIC): + drawn the draw: 1 once the scene's `_drawn_ok` latch is set (liquid >= `draw_min` while + seated on the reservoir); before that, the liquid in the barrel as a linear ramp + from empty (0) to `draw_min`. A milestone — the liquid is dispensed afterwards + dosed fraction over the three identical tubes: a tube inside the scene's `dose_band` + counts 1; below the band, the dose as a linear ramp from 0 to the band's lower + edge; pushed past the band it counts 0 — wells never un-fill, so an over-dose is + the scene's irreversible ruin of that tube. Live: an over-push drops a tube + parked the scene's `success()`: syringe lying at its home spot, on the shelf, settled + (`parked`) — GATED on the draw + all three doses. The syringe SPAWNS parked, so + the park only counts once the dosing it concludes is done (else it is free credit) +Success is the scene's own `success()` (drawn AND doses_ok AND parked) on the final state. +""" + +from __future__ import annotations + +from robobench.core import BaseGrader +from robobench.suites.puzzle.scenes.syringe_dosing import SyringeDosingScene + + +class SyringeDosingGrader(BaseGrader): + """A full draw from the reservoir, an in-band dose in each of the three tubes, and the syringe laid back at its spot. + + Ladder: full draw 0.20 · 1 dose 0.40 · 2 doses 0.60 · 3 doses 0.80 · parked 1.00 + """ + + SCENE = SyringeDosingScene + # dosed x3: three identical tubes, one unit of credit per tube — the same unit as the draw + # and the park (the egg-carton ladder: each egg a step, the lid the last step) + RUBRIC = (("drawn", 1, "once"), ("dosed", 3), ("parked", 1)) + scene: SyringeDosingScene + + def setup(self) -> None: + pass # every quantity is a scene ledger; nothing to capture at reset + + def check_success(self): + return self.scene.success() # drawn AND doses_ok AND parked — live + + # ---- rubric stages — each returns a (num_envs,) value in [0, 1] -------------------------- + def drawn(self): + import torch + + s = self.scene + ramp = (s.liquid() / s.cfg.draw_min).clamp(0.0, 1.0) + return torch.where(s.drawn(), torch.ones_like(ramp), ramp) + + def dosed(self): + import torch + + s = self.scene + lo, hi = s.cfg.dose_band + d = s.doses() # (num_envs, 3) fraction of capacity per tube + ramp = (d / lo).clamp(0.0, 1.0) + per_tube = torch.where(d > hi, torch.zeros_like(d), ramp) # in band -> ramp is 1 + return per_tube.mean(dim=1) + + def parked(self): + return self.scene.success().float() diff --git a/reference/vla/README.md b/reference/vla/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c584eb509da3c434bc7beff014a8cd8066be79fd --- /dev/null +++ b/reference/vla/README.md @@ -0,0 +1,189 @@ +# vla — from generated episodes to a trained, evaluated policy + +Everything downstream of data generation: bake raw episodes into a LeRobot +dataset, train a policy on it, drive the same sim closed-loop with the trained +policy. lerobot is vendored as a submodule so the whole chain lives in one +checkout at one pinned revision. + + convert/ raw episodes -> LeRobotDataset (one canonical Episode, many label conventions) + eval/ the sim behind a socket (Isaac side) + the lerobot eval plugin (client side) + lerobot/ huggingface/lerobot, submodule, pinned (see below) + +## Two venvs, one contract + +Isaac Sim is pinned to Python 3.11 / torch 2.7; lerobot needs Python >= 3.12 and its +own torch. They never share an interpreter — they talk over the socket protocol in +`eval/protocol.py` (stdlib + numpy only). So the repo has two environments: + +| venv | python | built by | runs | +|---|---|---|---| +| `.venv` | 3.11 | `scripts/bootstrap_isaaclab_5_1.sh` | robobench, data generation, `eval/serve.py`, `eval/replay_actions.py` | +| `.venv-lerobot` | 3.12 | `scripts/bootstrap_lerobot.sh` | `convert/convert.py`, `lerobot-train`, `lerobot-eval --env.type=cosigen` | + +Setup, from the repo root: + +```bash +git submodule update --init vla/lerobot # once (or clone with --recurse-submodules) +./scripts/bootstrap_lerobot.sh # creates .venv-lerobot, installs lerobot[training,pi,smolvla,diffusion] + the eval plugin +``` + +The bootstrap installs the submodule **editable**, so an edit under `vla/lerobot/src` +is live in the venv without a reinstall. Extras are a knob +(`COSIGEN_LEROBOT_EXTRAS=training,pi` for a smaller install); the defaults cover +every policy family we have trained on CoSiGen data (ACT, Diffusion, pi0/pi0.5, +SmolVLA). + +Video decode: lerobot's fast path is torchcodec, which dlopens FFmpeg's shared +libraries at import time and **silently falls back to pyav** (several times slower +dataloading) when it cannot. The bootstrap's verification step prints which one you +got. If it says torchcodec is not loadable, put an FFmpeg 5–7 `lib/` on +`LD_LIBRARY_PATH` (conda-forge `ffmpeg`, or your cluster's FFmpeg module) before +training. + +## Pinned revision + +`vla/lerobot` is pinned to upstream `huggingface/lerobot` @ `6adf5151` (v0.6.1 + 24, +lerobot 0.6.2 dev). This is the exact tree every CoSiGen policy result so far was +produced with; `convert/` and `eval/lerobot_env_cosigen/` are written against its +dataset v3.0 format, processor pipeline and `lerobot-eval` plugin discovery. Bumping +the pin is a deliberate change: re-run a bake (`convert/README.md`), a `check_load`, +and one closed-loop eval before committing a new SHA. + +## Walkthrough: fine-tune pi0.5 on a task and evaluate it closed-loop + +The bulb task (`assembly.bulb.franka.pink_ik`) as the worked example; every other task is +the same five steps with a different preset. Paths in `<…>` are yours. Isaac steps use +`.venv`, lerobot steps use `.venv-lerobot`; a step never mixes the two. + +### 1. Generate episodes (Isaac venv) + +`data_engine/scripts/generate.py` runs the solved strategy for a task, records full state + +commands every tick, and renders the declared training cameras. One call = one batch of +`--num_envs` episodes; a campaign is many batches under one ``: + +```bash +.venv/bin/python data_engine/scripts/generate.py --headless \ + --batch b0 --num_envs 8 --seed 0 --nominal --render +``` + +Each episode lands in `/data//ep_XXXX/` (`traj.npz`, `meta.json`, and +`imgs/.mp4` + `imgs/render_.json` per camera). See `data_engine/` and the scene's +`CAMERAS` declaration for which views are rendered. + +### 2. Bake a LeRobot dataset (lerobot venv) + +```bash +.venv-lerobot/bin/python vla/convert/convert.py \ + --repo-id /_ --control_freq 15 --workers auto +``` + +Defaults: `--control_space joint_target` (the commanded joint targets; presses/squeezes survive +as target offsets), successful episodes only, all rendered views. The dataset lands at +`/datasets//` with **`meta/bake.json`**, the provenance stamp the eval loads +the sim from in step 4. Reasons for these two choices, and the label caveats, are in +`convert/README.md`. The 15 Hz `joint_target` bake is what produced every bulb result; +achieved-joint labels (`joint_pos`) at 60 Hz gave 0 % for pi0.5. + +Push to the Hub if training runs elsewhere (`lerobot-train` also accepts a local dir): + +```bash +.venv-lerobot/bin/hf upload / /datasets/ --repo-type dataset +# then tag it, or lerobot-train refuses to load it ("revision not found"): +.venv-lerobot/bin/python -c "from huggingface_hub import HfApi; HfApi().create_tag('/', tag='v3.0', repo_type='dataset')" +``` + +### 3. Fine-tune pi0.5 (lerobot venv, 1 GPU) + +`lerobot/pi05_base` is built on PaliGemma, a gated checkpoint: accept the license on the Hub +once and log in (`hf auth login`). Then: + +```bash +.venv-lerobot/bin/lerobot-train \ + --dataset.repo_id=/ \ + --dataset.root=/datasets/ \ # omit to pull from the Hub + --dataset.video_backend=torchcodec --tolerance_s=0.005 \ + --policy.type=pi05 --policy.pretrained_path=lerobot/pi05_base \ + --policy.normalization_mapping='{"ACTION": "MEAN_STD", "STATE": "MEAN_STD", "VISUAL": "IDENTITY"}' \ + --policy.n_action_steps=10 \ + --policy.empty_cameras=1 \ + --policy.freeze_vision_encoder=false --policy.train_expert_only=false \ + --policy.gradient_checkpointing=true --policy.dtype=bfloat16 --policy.device=cuda \ + --policy.push_to_hub=false \ + --steps=30000 --policy.scheduler_decay_steps=30000 --save_freq=5000 \ + --batch_size=64 --num_workers=4 --seed=1000 \ + --output_dir=/pi05_ --job_name=pi05_ \ + --wandb.enable=false +``` + +What the non-obvious flags do: + +| flag | why | +|---|---| +| `--tolerance_s=0.005` | lerobot's default 1e-4 is below float32 timestamp resolution deep into a long v3 video file; without this, training dies with `FrameTimestampError` ~50 min in. A short smoke run cannot catch it. | +| `--policy.empty_cameras=1` | pi0.5 expects 3 image slots; our bakes have 2 cameras (front + wrist), so one slot is padded. Set to `3 - `. | +| `normalization_mapping` | MEAN_STD on state/action (the pi0.5 recipe); IDENTITY on images since the model normalizes internally. | +| `--policy.n_action_steps=10` | the re-plan horizon at inference; a training-time default that eval may override (below). | +| `scheduler_decay_steps = steps` | decay the LR to the end. Across every model we trained, only the fully decayed final checkpoint produced successes. | +| `freeze_vision_encoder=false`, `train_expert_only=false` | full fine-tune. Fits one 80–96 GB GPU at batch 64 with gradient checkpointing + bf16. | + +Host memory: budget ~240 GB RAM for the pi0.5 dataloader over a long run (RSS creeps). +Resume after preemption with `lerobot-train --config_path=/checkpoints/last/pretrained_model/train_config.json --resume=true`. +A 20-step smoke (`--steps=20 --save_freq=10`) checks the config and that the checkpoint loads +before you commit a GPU-day. + +### 4. Evaluate closed-loop (two terminals, one GPU node with RT cores) + +The sim is pinned to the dataset's `bake.json`, so it is, by construction, the world the policy +was trained on: same preset, control space + rate, controller gains, cameras, task sentence. + +```bash +# terminal 1 — Isaac venv. Boots the scene once and stays warm across runs. +.venv/bin/python vla/eval/serve.py /datasets//meta/bake.json \ + --headless --port 5555 --record-dir /rollouts + +# terminal 2 — lerobot venv. Declares nothing about the sim; it binds cameras/dims/fps +# from the server handshake. +.venv-lerobot/bin/lerobot-eval \ + --policy.path=/pi05_/checkpoints/030000/pretrained_model \ + --policy.device=cuda --policy.n_action_steps=10 \ + --env.type=cosigen --env.port=5555 --env.max_episode_seconds=240 \ + --eval.n_episodes=32 --eval.batch_size=1 --eval.use_async_envs=false \ + --seed=1000 --output_dir= +``` + +Knobs that matter: + +- `--env.max_episode_seconds` is the per-episode cap in **sim seconds** (rate-independent). + Pick it from the demos: bulb successes all end by ~90 s, so 240 s is generous and anything + longer only burns GPU on failures. +- `--policy.n_action_steps` is a legitimate eval axis (how many chunk steps execute before + re-planning). Sim time freezes while the policy thinks, so inference latency is not modeled. +- `--seed` selects the scene randomization per episode; shards of one eval use disjoint seeds. + `serve.py --init dataset --init-batch /data/` starts from recorded episode + states instead (episode = seed % n), the generalization-eval mode. +- `--record-dir` on the server writes per-episode state/action rollouts next to lerobot's videos. +- For long contact tasks, run 1 episode per job with a wall-clock watchdog and count a killed + episode as a failure. On the bulb, ~20–40 % of failed episodes wedge the bulb in the socket rim + and PhysX drops to seconds per step; re-rolling those biases success upward. + +### 5. Read the result + +`/eval_info.json`: + +- `overall.pc_success` = success rate. Success is the suite grader's verdict + (`robobench/suites//grader/`), not a scene heuristic. +- `per_task[0].metrics.max_rewards` = per-episode **peak grader progress** on the same 0..1 + rubric generation reports as `score` (bulb: picked 0.2 + engaged 0.2 + threaded 0.6, so a + run stuck at 0.40 grasped and seated the bulb but never threaded it). `sum_rewards` is the + area under the progress curve. +- `/videos/` has one mp4 per episode. Watch the failures before trusting a number. + +Reference, bulb / `joint_target` 15 Hz / `pi05_base` / 30k steps / nas 10: 25–38 % success +depending on checkpoint and seeds; `pi05_droid` init: 0 % (all episodes plateau at 0.40). + +## Where to read next + +- `convert/README.md` — the bake CLI, the two decisions (`--control_freq`, + `--control_space`), the label caveats. +- `eval/README.md` — `load_sim`, open-loop replay certification, and the + two-terminal closed-loop `lerobot-eval` recipe. diff --git a/reference/vla/convert/README.md b/reference/vla/convert/README.md new file mode 100644 index 0000000000000000000000000000000000000000..6f7e1db9176ff72ca9260403f3f19adee57cd579 --- /dev/null +++ b/reference/vla/convert/README.md @@ -0,0 +1,178 @@ +# convert — raw episodes → training datasets + +One canonical intermediate, many conventions as pure projections of it. Every raw +source — a sim episode today, a real-robot log tomorrow — is read into the same +`Episode` (episode.py); a convention (conventions.py) projects it to (state, action) +at a control rate; convert.py streams the result into a LeRobotDataset that both +π/openpi and GR00T consume. Sim-real co-training data goes through identical code by +construction; a new source costs one reader, a new label costs one pure function. + + episode.py the canonical Episode + the sim reader + conventions.py label conventions (projections) + filters.py opt-in tick filters (idle removal) + convert.py the bake CLI + +## Bake + + .venv-lerobot/bin/python vla/convert/convert.py \ + <…/data_gen/> --repo-id cosigen/bulb_franka_osc \ + [--control_space joint_vel] [--control_freq 15] (default space: joint_target) [--batches …] [--cams front wrist] \ + [--root ] [--task "…"] [--include-failures] \ + [--workers auto] [--max-video-file-seconds 800] + +Views come from each episode's `render_.json` (`--cams` narrows); successful +episodes only by default; the dataset lands at `/datasets/` — +datasets stay with the campaign that produced them. Videos are decoded sequentially, +an episode never sits in RAM. + +**Parallelism.** A bake costs ~2 min/episode (decode → PNG staging → h264 encode). +`--workers N` (or `auto` = `SLURM_CPUS_PER_TASK` / `os.cpu_count()`) splits the episodes +across N worker processes, each baking a temporary shard (`.shards/wNN`), then +merges them with lerobot's `aggregate_datasets` into `--root`, applies the video-duration +cap (caveat 7) to the merged packing, verifies it, and deletes the shards. One command, +one dataset, whether you have 1 CPU or 48: `sbatch -c 32 … convert.py … --workers auto`. +Sequential mode (`--workers 1`, the default) uses lerobot's async image writer. +Multi-node scale-out = run the sharded bake per node and merge the same way +(`hpc/bulb_ik/merge_shards.py` is the template). + +## The two decisions + +Everything else is derived; a bake is fully specified by: + +**`--control_freq`** — the label control frequency. Omitted = the native control rate (one tick +per latch; precisely, the video fps — equal to the solver rate for matched-regime +renders). A lower rate must divide the fps exactly and be an integer (60 → 30, 20, +15, 12, 10). + +**`--control_space`** — what the action column means (default: `joint_target`). State is +always the same (`[arm q, gripper]` at the tick whose image the policy sees): + +| convention | action at tick t | semantics | +|-------------|----------------------------------------|-----------| +| `joint_pos` | achieved `q` at t+1/rate + gripper | absolute tracking target; GR00T / LeRobot school; the sim-real workhorse — any joint PD executes it | +| `joint_vel` | `(q_next − q)·rate` + gripper | π₀.₅-DROID exactly (15 Hz). NOT the measured `joint_vel`: the finite difference — "the average velocity that reaches the next pose", the form a tracker integrates | +| `joint_target` | the COMMANDED joint targets in force at t + commanded gripper closedness (UNCLAMPED: >1 = squeeze) | THE DEFAULT — controller intent: presses/squeezes survive as sustained target offsets that achieved labels flatten. Position-mode campaigns only (joint/diff_ik/pink_ik; the recorder omits the channel under a torque-mode arm, and this bake then refuses loudly — use joint_pos/joint_vel/raw_cmd for osc campaigns) | +| `raw_cmd` | the recorded controller command, verbatim | LIBERO/MimicGen school; the sim-only matched-controller benchmark arm — deploy through the SAME controller | + +At a fixed rate these are the same information (v = Δq·rate), so conventions are +dialects, not different data. Whatever the convention, sim bakes also carry the +verbatim command as a `raw_command` column: contact intent (a press = a sustained +offset that achieved kinematics hide) survives every projection, and later +relabelings (e.g. commanded joint targets via IK) never need the raw files again. + +**Gripper** is one scalar CLOSEDNESS in [0, 1] (0 = fully open, 1 = fully closed), +normalized by each finger joint's travel (`episode.FINGER_TRAVEL`). It is achieved, +not commanded — squeeze force lives only in `raw_command`. + +## Dataset schema + +Keys are the superset-friendly ones: `observation.images.` (video, native +render resolution — training transforms resize), `observation.state`, +`action`, `raw_command` (sim only), `task` (the scene's Goal sentence, `--task` +overrides). This satisfies the strict consumer natively and the flexible one +trivially: + +- **GR00T** hardcodes these key names and needs `meta/modality.json` (written by the + bake: named parts as start/end slices of the concatenated vectors, video key map). +- **π/openpi** is key-agnostic: each fine-tune declares a ~10-line repack transform + mapping any keys to the model's inputs. Its other artifact, `norm_stats.json`, + is generated by openpi's `compute_norm_stats` at training setup — derived, not + authored; the dataset needs nothing extra. + +The bake also writes `meta/bake.json` — THE provenance stamp: convention, rate, +part maps, gripper convention, the recorded control law, source episodes + git +shas. The stamp is the single source of truth an eval bridge reads to build the +matching executor (`raw_cmd` → the same OSC env.step; `joint_pos`/`joint_vel` → one +joint-PD tracking wrapper at the stamped rate), so train/eval convention drift is +structurally impossible. + +## Video encoding + +Dataset videos are written by lerobot's encoder; the bake exposes its knobs and picks +defaults for *training data that many frameworks will read*, which differ from lerobot's +own storage default (`libsvtav1`, `crf 30`, `g=2`): + +| flag | default | why | +|---|---|---| +| `--vcodec` | `h264` | decodes everywhere (torchcodec, pyav, decord, every NVDEC generation); AV1 needs dav1d / Ampere+ | +| `--crf` | `23` | x264's standard quality point (18 ≈ visually lossless, ~35 % more bytes) | +| `--gop-seconds` | `0.25` | keyframe interval in **seconds** (`g = round(fps·s)`, min 1): worst-case seek cost is constant across control rates — g=15 at 60 Hz, g=4 at 15 Hz | +| `--pix-fmt` | `yuv420p` | the universally decodable layout | + +Measured (torchcodec, 1 thread, 640×480 @ 60 fps, one 175 s episode, front camera): + +| encode | size | random-access decode | sequential decode | +|---|---|---|---| +| lerobot default `libsvtav1 crf30 g=2` | ~90 MB | 330 fps | 850 fps | +| `h264 crf23 g=2` | 75 MB | 355 fps | 1020 fps | +| **`h264 crf23 g=15` (this default)** | **17 MB** | 264 fps | 2200 fps | +| `h264 crf18` long GOP (render masters) | 11 MB | 47 fps | 1790 fps | + +Random access is governed by the GOP, not the codec: the render masters' long GOP is what +makes them slow to sample, and lerobot's g=2 buys the last 20 % of seek speed with 4–5× the +bytes. Policies read short clips (a few observation frames + an action chunk), where the +GOP-15 h264 is the fastest of all. The chosen encoder is stamped into `meta/bake.json` +(`video_encoder`) and lerobot's own `meta/info.json`. Pass `--vcodec libsvtav1 --gop-seconds 0.034` +to reproduce lerobot's default exactly. + +Decoder note: in a conda env torchcodec needs the env's FFmpeg on the loader path +(`export LD_LIBRARY_PATH=$CONDA_PREFIX/lib:$LD_LIBRARY_PATH`) or lerobot silently falls back +to pyav — same frames, lower throughput. + +## The control law travels with the data + +`raw_cmd` numbers only mean anything under the controller that interpreted them +(pos/rot scale, re-anchoring, kp/kd, gripper drive stiffness). Generation stamps +that law as a `controller` block in each episode's `meta.json` (since 2026-08-18); +the bake copies it into `bake.json` and REFUSES pools whose blocks differ — +mixed presets or sim+real teleop must be separate bakes, staying partitionable. +Earlier batches lack the block (`controller: null`); recovering their law means +reading the batch's own solve.py, because solves override preset defaults live +(the bulb campaign sets rot_scale 0.15 over the preset's 0.097 — the exact +silent drift stamping exists to catch; kp 150/600, pos_scale 0.02 and gripper +drive stiffness 8000 came from setup writes too). + +## Extending + +- **New gripper**: one `FINGER_TRAVEL` entry (joint name → travel). Unknown finger + joints are a hard error — a guessed travel silently corrupts every gripper label. + Fingers are found by name marker ("finger"/"gripper"); an oddly named vendor + joint means switching that lookup to an explicit per-robot registry entry. +- **New arm**: free — joints, dims and names flow from the render contract. +- **New source** (real robot): one reader returning `Episode`; nothing downstream + changes. +- **New convention**: one pure function `Episode → Projected` in conventions.py + (dexterous hands would enter here: per-finger parts instead of the closedness + scalar — the named-parts machinery already supports it). + +## Caveats + +1. **Contact flattening**: during a press, achieved q barely moves — `joint_pos` + says "stay", `joint_vel` says ~0, while the intent is *push*. `raw_command` is + the recovery path; a joint-convention policy stalling exactly at contact phases + is this. Same for gripper squeeze force. +2. **Idle/settle phases** are kept by default. `--filter-idle` (opt-in) drops the + DEAD ticks only: robot static AND every object static (pose delta + recorded + velocities ~0) AND no command intent. The intent guard (|EE offset| >= 0.1) + keeps static-but-pushing press ticks; active settling has nonzero velocities + and is kept; the first tick of each idle run stays as the arrive-and-settle + cue. ~25% of the bulb episode is dead ticks. The classic BC freeze failure + mode is the reason to turn it on; survivorship of presses is why it's safe. +3. The **last tick's action is "stay"** by construction (clamped lookahead). +4. **Norm stats**: near-constant dims explode after quantile normalization (openpi + warns about this) — inspect `norm_stats.json` before the first training run. +5. **GR00T container version**: our lerobot writes the v3 layout; GR00T docs + describe v2. Verify (or down-convert) before the first GR00T run. +6. **Success-only** by default — survivor bias, correct for BC + (`--include-failures` exists). +7. **Video file duration vs LeRobot's timestamp check.** LeRobot stores frame + timestamps as float32 and verifies each decoded frame within `tolerance_s=1e-4`. + Past 1024 s into a video file the float32 step is 1.2e-4 s > the tolerance, so + two roundings of the same instant fail the check and training dies mid-run + (`FrameTimestampError`, ~50 min in; a short smoke never draws such a frame). + LeRobot only caps files by MB (default 200 MB ≈ 30 min at 15 fps / 640×480 h264), + so `--max-video-file-seconds` (default 800) measures the bitrate on the first + episode, derives the MB cap from it, and an ffprobe pass fails the bake if any + file still exceeds 1000 s. The merge step (`aggregate_datasets`) repacks and + must be given the same MB cap (see the hpc merge script). Consumers should still + pass `--tolerance_s=0.005` (13× finer than the 66.7 ms frame period) as a belt. diff --git a/scripts/build_browser.py b/scripts/build_browser.py new file mode 100644 index 0000000000000000000000000000000000000000..1c3a0b03135b2de5a249513e32f73db30ab5cddf --- /dev/null +++ b/scripts/build_browser.py @@ -0,0 +1,93 @@ +"""Build a static HF browser with a full scene catalog and a Franka camera guide.""" +from pathlib import Path +import json,re,sys +ROOT=Path(__file__).resolve().parents[1] +DATA=ROOT if (ROOT/'provenance/source.json').exists() else ROOT/'dataset' +OUT=ROOT/'space' + +JS=r''' +const DATA=JSON.parse(document.getElementById('catalog').textContent); +const esc=x=>String(x??'').replace(/[&<>"']/g,c=>({'&':'&','<':'<','>':'>','"':'"',"'":'''}[c])); +const media=p=>DATA.media_base+p.split('/').map(encodeURIComponent).join('/'); +const file=p=>DATA.file_base+p.split('/').map(encodeURIComponent).join('/'); +const link=(u,t)=>`${esc(t)}`; +const tasks=DATA.tasks,groups=[...new Set(tasks.map(t=>t.category))]; +let current=null,query='',group='all',mode='combined'; +function drawList(){ + document.getElementById('groups').innerHTML=['all',...groups].map(g=>``).join(''); + const numeric=/^(C)?\d+$/i.test(query.trim()); + const shown=tasks.filter(t=>(numeric||group==='all'||t.category===group)&&(numeric?t.catalog_number===Number(query.replace(/^C/i,'')):`${t.scene_key} ${t.official_scene_id} ${t.name} ${t.instruction}`.toLowerCase().includes(query.toLowerCase()))); + document.getElementById('listCount').textContent=`${shown.length} / 28`; + document.getElementById('taskList').innerHTML=shown.map(t=>``).join('')||'
未找到场景。这里的 C01–C28 是官网目录辅助编号，不是 task_index。
'; + document.querySelectorAll('[data-group]').forEach(b=>b.onclick=()=>{group=b.dataset.group;drawList()});document.querySelectorAll('[data-task]').forEach(b=>b.onclick=()=>{select(b.dataset.task);window.scrollTo({top:0})}); +} +function select(key,historyUpdate=true){document.querySelectorAll('video').forEach(v=>v.pause());if(historyUpdate)history.replaceState({},'',`?task=${encodeURIComponent(key)}`);current=tasks.find(t=>t.scene_key===key);drawList();if(key==='camera-guide')drawCamera();else drawTask();document.querySelector('.task-link.active')?.scrollIntoView({block:'nearest'});} +function player(p,poster='',aspect=''){return ``} +function pageHeader(){return `
EmbodiedSWE / 28 SCENES / 30 DATA EPISODES / 26 WEBSITE CLIPS
${link(DATA.repo_url,'HF 数据仓库 ↗')}${link('https://embodiedswe.github.io/#benchmark','官方项目 ↗')}腕部相机说明 ↗
`} +function episode(s){ + const n=s.views.length; + const content=mode==='combined'?`
${s.views.map(v=>`${esc(v.label)}`).join('')}
${player(s.composite_path,s.preview_path,`${320*n}/268`)}
`:`
${s.views.map(v=>`
${esc(v.label)}
${player(v.path,'','4/3')}
`).join('')}
`; + return `
EPISODE ${s.episode_index} · source task_index=${s.task_index}
${s.frame_count} frames · ${s.fps} FPS · ${(s.frame_count/s.fps).toFixed(2)} s
OFFICIAL HF DATA
该条数据的原始 instruction

${esc(s.instruction)}

${esc(s.source_repo)}
${content}
${s.views.map(v=>link(file(v.path),v.label+' MP4')).join('')}${link(media(s.trajectory_path),'官方动作 / 状态')}${link(file(s.composite_path),'同步 MP4 ↗')}
${mode==='original'?'
':''}
`; +} +function drawTask(){ + if(!current){document.getElementById('main').innerHTML=pageHeader()+'

请选择官网场景

';bindCameraLinks();return} + const t=current,idx=tasks.indexOf(t),samples=DATA.samples.filter(s=>s.scene_key===t.scene_key); + document.title=`C${t.catalog_number} · ${t.name} · EmbodiedSWE`; + let html=pageHeader()+`
C01–C28 是本浏览页的官网目录辅助编号。官方任务 ID 使用 suite.scene；每个 HF 数据库的 task_index / episode_index 单独编号，不能跨库混用。
${esc(t.category)} · ${esc(t.difficulty)}
C${String(t.catalog_number).padStart(2,'0')}

${esc(t.name)}

${esc(t.official_scene_id)}
评分标准 ↓
官网任务目标 / instruction

${esc(t.instruction)}

官网列出的 evaluated on${esc(t.catalog_embodiments)}
公开示教${samples.length?samples.length+' 条完整 HF episode':'未找到该场景的公开 HF 训练示教'}
评分来源${link(t.grader_url,'当前官方 grader 源码 ↗')}
`; + if(samples.length)html+=`

官方多相机示教

完整 MP4，保持上游帧数与 FPS。${esc(samples[0].views.map(v=>v.label).join(' → '))}
单臂 Franka 的 WRIST 是一个腕部相机；没有虚构 LEFT / RIGHT。额外场景相机按实际名称展示。${link('https://huggingface.co/datasets/'+t.dataset_source.repo_id,'完整官方源数据 ↗')}
${samples.map(episode).join('')}
`; + if(t.gallery)html+=`

官网演示 MP4

${esc(t.name)} · WEBSITE DEMORECORDED RENDER VIEW
${player(t.gallery.path)}
${link(file(t.gallery.path),'MP4 文件 ↗')}${link(t.gallery.url,'官网原文件 ↗')}

官网播放器设为 ${t.gallery.website_playback_rate}×；这里默认按公开 MP4 文件的 1× 播放。该片段没有提供配套腕部录像或动作轨迹，不能当成多相机训练 episode。

`; + else html+='
官网暂无该场景的展示 MP4。

目前未找到对应的公开 HF 示教，因此保留任务说明与评分源码，不放入不相关视频。

'; + html+=`

Rubric · 评分与成功条件

${esc(t.grader_summary)}

${t.rubric.map(r=>``).join('')}
Stage权重 / 占比记录模式
${esc(r.stage)}${r.weight} / ${(r.normalized_weight*100).toFixed(1)}%${esc(r.mode)}

progress 是各阶段值的归一化权重和，范围 [0,1]。live 读当前值；once 记录历史最好值；final 在交付结束时评估。score 根据过程峰值与末端完整评估决定，success 还要满足 check_success() 和 final 阶段要求。

这里展示当前公开代码的评分规则，不是本条示教的实测成绩。原始训练库可能使用更早的 grader 版本；示例没有公开逐条 score / success，因此留空。
阶段定义与源码中的成功检查
${esc(t.grader_description)}
${esc(t.success_source)}
${t.rubric_variants?`
不同 goal 的 rubric 变体
${esc(JSON.stringify(t.rubric_variants,null,2))}
`:''}
${link(t.grader_url,'官方评分源码 ↗')}${link(DATA.repo_url,'精选数据仓库 ↗')}

Code revision: ${esc(DATA.source.code_revision)}

官网的 7 个展示标签与代码的 6 个 suite 并非同一层次：Liquid 演示分布于 deformable / puzzle 代码 suite。数据仅按需读取，不包括 assets 或模型权重。

`; + document.getElementById('main').innerHTML=html;bindCameraLinks();bindVideos(); + for(const [id,step] of [['previous',-1],['next',1]])document.getElementById(id).onclick=()=>{query='';document.getElementById('search').value='';select(tasks[idx+step].scene_key);window.scrollTo({top:0})}; + document.querySelectorAll('[data-mode]').forEach(b=>b.onclick=()=>{const y=scrollY;document.querySelectorAll('video').forEach(v=>v.pause());mode=b.dataset.mode;drawTask();window.scrollTo({top:y})}); +} +function bindVideos(){ + document.querySelectorAll('.episode').forEach(el=>{const vs=[...el.querySelectorAll('video')],error=el.querySelector('.error');vs.forEach(v=>v.onerror=()=>{error.hidden=false;error.textContent='当前 MP4 加载失败，可用下方文件链接打开。'});if(!el.querySelector('[data-play]'))return;let timer=null;const pause=()=>{vs.forEach(v=>v.pause());clearInterval(timer);timer=null};el.querySelector('[data-play]').onclick=async()=>{try{vs.forEach(v=>{if(v.readyState)v.currentTime=vs[0].currentTime});await Promise.all(vs.map(v=>v.play()));clearInterval(timer);timer=setInterval(()=>{if(!el.isConnected){clearInterval(timer);return}for(const v of vs.slice(1))if(v.readyState&&Math.abs(v.currentTime-vs[0].currentTime)>.2)v.currentTime=vs[0].currentTime},300)}catch(e){pause();error.hidden=false;error.textContent='请点击各播放器或选择同步合并 MP4。'}};el.querySelector('[data-pause]').onclick=pause;el.querySelector('[data-reset]').onclick=()=>{pause();vs.forEach(v=>{if(v.readyState)v.currentTime=0})};vs[0].addEventListener('pause',pause);vs[0].addEventListener('ended',pause);}); +} +function bindCameraLinks(){document.querySelectorAll('.camera-link').forEach(a=>a.onclick=e=>{e.preventDefault();select('camera-guide');window.scrollTo({top:0})})} +function drawCamera(){ + document.title='Franka wrist camera · EmbodiedSWE'; + const codeBase='https://github.com/EmbodiedSWE/EmbodiedSWE/blob/'+DATA.source.code_revision; + const guide='https://franka.de/hubfs/Setup%20Guide%20for%20Camera%20Mount%20on%20Franka%20Robots_1.1_EN.pdf?hsLang=en'; + document.getElementById('main').innerHTML=pageHeader()+`
CAMERA / SIMULATION & HARDWARE

Franka 腕部相机：需要吗，装在哪？

先分清使用方式

机械臂本身不要求必须装腕部相机。EmbodiedSWE 的编码代理可直接操作模拟器状态；已发布的视觉策略数据则包含 wrist，复现这些 VLA 输入时需要匹配原相机配置。

使用场景腕部相机
Coding-agent benchmark不是原始 env 接口的硬性图像输入。get_states / describe_stage 等可读取模拟器状态，图像观察由包装器决定。
复现已发布 VLA 数据 / 策略本次精选的 10 个源库都包含 wrist。闭环评估须匹配训练时的相机名称、外参、分辨率和帧率；去掉 wrist 需要重新适配与验证。
实机自己的新任务按感知方案选择外部相机、腕部相机或二者组合。腕部相机随末端移动，适合补充近距离手指 / 工件视角。

1. EmbodiedSWE 的仿真安装

官方 Franka 模型是 Panda + 平行夹爪。相机作为子节点挂在 panda_hand，不是挂在指尖或世界坐标中；它随 hand 的位姿一起移动。代码注释将它描述为 D435 支架 / DROID 风格，位于手旁并沿接近方向观察。

panda_hand随机器人移动的父 link手旁的固定支架 / 光学位置wrist光轴沿 hand 的 +Z朝夹爪接近工件的方向坐标关系示意；非机械加工图，不代表实机朝向或螺丝孔位。
参数源码值
parent linkpanda_hand
eye（光心，link 局部坐标）(0.0465, -0.0200, 0.0360) m = (46.5, -20, 36) mm
target（局部 look-at 点）(0.0465, -0.0200, 0.3000) m
focal10.5 mm；默认 20.955 mm 水平孔径下，约 90° 水平视场。
prim（单臂默认）{ENV_REGEX_NS}/Robot/panda_hand/wrist
${link(codeBase+'/robobench/robots/franka.py','Franka 相机声明 ↗')}${link(codeBase+'/data_engine/engine/replay.py','相机挂接与 look-at 实现 ↗')}

2. 实机安装：Franka 官方支架

官方 FR3 指南提供 D435/D435i 专用 STL 与通用相机支架。相机通过支架固定在 Franka Hand 与末端法兰附近的一侧，不是装在会开合的手指上。先固定相机到支架，再把支架组件接到 Hand / 末端法兰的指定 M6 安装位置；D435 相机侧使用 M3 螺钉，具体螺钉和扭矩依型号查指南。

这份指南的适用型号写明 FR3；EmbodiedSWE 的仿真资产是 Panda。Panda、FR3、Franka Hand、Robotiq 夹爪并非同一套机械接口，不能直接照抄螺钉长度或把仿真 eye 坐标当作安装孔位。

实机应根据夹爪和相机型号选兼容支架，让镜头覆盖手指间的操作区，并给数据线保留活动余量。安装后的相机内参 / 手眼外参应实际标定；不能直接拿仿真坐标替代。

${link(guide,'官方安装 PDF（图 1 / 图 4）↗')}${link('https://franka.de/models-and-simulation','官方 CAD / STL 下载 ↗')}${link('https://download.franka.de/camera_mount_guide.zip','官方可打印支架 ZIP ↗')}
${link(codeBase+'/robobench/README.md','编码代理 env 接口 ↗')}${link(codeBase+'/vla/README.md','VLA 训练 / 评估相机契约 ↗')}

源码与厂商文档核查日期：2026-10-05。图中示意仅解释位置关系；机械尺寸以对应型号官方资料为准。

`; + bindCameraLinks(); +} +document.getElementById('search').oninput=e=>{query=e.target.value;drawList()}; +select(new URLSearchParams(location.search).get('task')??'bulb',false); +''' + +def build(): + tasks=json.loads((DATA/'provenance/tasks.json').read_text(encoding='utf-8'));samples=json.loads((DATA/'provenance/samples.json').read_text(encoding='utf-8'));source=json.loads((DATA/'provenance/source.json').read_text(encoding='utf-8')) + preview='--preview' in sys.argv + pubpath=ROOT/'publication.json' if (ROOT/'publication.json').exists() else DATA/'provenance/media_publication.json' + pub=json.loads(pubpath.read_text(encoding='utf-8')) if not preview else {'dataset_commit_sha':'preview'} + revision=pub['dataset_commit_sha'] + payload=dict(tasks=tasks,samples=samples,source=source,repo_url='https://huggingface.co/datasets/Travor278/EmbodiedSWE-Task-Examples',media_base=f'https://huggingface.co/datasets/Travor278/EmbodiedSWE-Task-Examples/resolve/{revision}/',file_base=f'https://huggingface.co/datasets/Travor278/EmbodiedSWE-Task-Examples/blob/{revision}/') + if preview:payload.update(media_base='http://127.0.0.1:8772/dataset/',file_base='http://127.0.0.1:8772/dataset/') + template=(ROOT/'browser/base.html').read_text(encoding='utf-8');template=re.sub(r'',template,flags=re.S);template=re.sub(r'',lambda m:'',template,flags=re.S);template=template.replace('RoboDojo','EmbodiedSWE').replace('工具演示','相机说明') + extra='.task-link small{display:block;font-size:8px;color:#8eaa93;letter-spacing:.6px;margin-top:4px}.big-id{font-size:48px;letter-spacing:-2px}.benchmark-note,.data-source-note{font-size:11px;line-height:1.8;padding:13px 16px;background:#e8ece1;color:var(--muted);margin-bottom:20px;border:1px solid var(--line)}.episode-instruction{padding:14px 16px;background:#faf9f1;border-bottom:1px solid var(--line);font-size:12px;line-height:1.65}.episode-instruction p{margin:7px 0}.episode-instruction span{font-size:10px;color:var(--muted);overflow-wrap:anywhere}.metadata{padding:14px 17px;border:1px solid var(--line);background:var(--white);margin:12px 0;font-size:12px}.metadata summary{cursor:pointer}.metadata pre{white-space:pre-wrap;overflow-wrap:anywhere;font-size:11px;line-height:1.7}.camera-diagram{margin:18px 0;padding:12px;background:var(--white);border:1px solid var(--line)}.camera-diagram svg{width:100%;height:auto}.toplinks{flex-wrap:wrap}.task-heading{flex-wrap:wrap}.combined video{aspect-ratio:auto}.facts strong{overflow-wrap:anywhere}' + template=template.replace('',extra+'');encoded=json.dumps(payload,ensure_ascii=False,separators=(',',':')).replace('0: + key=self.pos//self.block + if key not in self.cache: + start=key*self.block;end=min(self.size,start+self.block)-1 + # Resolve the signed CDN address once, then issue bounded ranges + # directly. Repeated HF redirects dominate small-range latency. + r=SESSION.get(self.resolved_url or self.url,headers={'Range':f'bytes={start}-{end}'},timeout=(20,120)) + if r.status_code in [401,403] and self.resolved_url: + self.resolved_url=None + r=SESSION.get(self.url,headers={'Range':f'bytes={start}-{end}'},timeout=(20,120)) + r.raise_for_status();self.resolved_url=r.url + assert r.status_code==206 and r.headers.get('Content-Range','').startswith(f'bytes {start}-'), 'Range unsupported; full-shard download refused' + self.cache[key]=r.content;self.transferred+=len(r.content) + if len(self.cache)>32:self.cache.popitem(last=False) + b=self.cache[key];offset=self.pos%self.block;take=min(n,len(b)-offset) + if take<=0:raise EOFError('truncated range') + out.append(b[offset:offset+take]);self.pos+=take;n-=take + return b''.join(out) + +def prepare(): + if all((OUT/'provenance'/f).exists() for f in ['tasks.json','samples.json','source.json']):print('Using retained source-pinned manifests.');return + from huggingface_hub import HfApi + api=HfApi();site=(WORK/'site.js').read_text(encoding='utf-8');gallery=read(WORK/'gallery.json') + block=re.search(r'var CATALOG = \[(.*?)\n \];',site,re.S).group(1) + catalog=re.findall(r"\['([^']*)','([^']*)','([^']*)',([ABG]),'([^']*)'\]",block);assert len(catalog)==28 + code_rev=subprocess.check_output(['git','-C',str(CODE),'rev-parse','HEAD'],text=True).strip() + tasks=[];samples=[] + for number,(suite,web_scene,objective,emb,difficulty) in enumerate(catalog,1): + key={'SO_101':'so101'}.get(web_scene,web_scene.lower());clip=next((c for c in gallery if c['scene']==key),None) + grader_suite,grader_name=GRADERS[key].split('/') + relative=f'robobench/suites/{grader_suite}/grader/{grader_name}.py';src=(CODE/relative).read_text(encoding='utf-8');tree=ast.parse(src);cls=next(n for n in tree.body if isinstance(n,ast.ClassDef) and any(isinstance(a,ast.Assign) and getattr(a.targets[0],'id','')=='RUBRIC' for a in n.body)) + rubric_expr=next(a.value for a in cls.body if isinstance(a,ast.Assign) and getattr(a.targets[0],'id','')=='RUBRIC') + if isinstance(rubric_expr,ast.Subscript): + variants=ast.literal_eval(next(a.value for a in cls.body if isinstance(a,ast.Assign) and getattr(a.targets[0],'id','')=='RUBRICS'));rubric=variants[ast.literal_eval(rubric_expr.slice)] + else:rubric=ast.literal_eval(rubric_expr);variants=None + total=sum(x[1] for x in rubric) + stages=[dict(stage=x[0],weight=x[1],normalized_weight=x[1]/total,mode=x[2] if len(x)>2 else 'live') for x in rubric] + success=next(n for n in cls.body if isinstance(n,ast.FunctionDef) and n.name=='check_success') + task=dict(catalog_number=number,scene_key=key,official_scene_id=f'{grader_suite}.{key}',category=suite,name=clip['name'] if clip else {'shoe_knot':'Shoelace half-knot','dice':'Food dicing'}[key],instruction=objective,difficulty=difficulty,catalog_embodiments={'A':'Franka / xArm7 / Gen3','B':'Bimanual Franka','G':'Unitree G1'}[emb],rubric=stages,rubric_variants=variants,grader_description=ast.get_docstring(tree),grader_summary=ast.get_docstring(cls),success_source=ast.get_source_segment(src,success),grader_source_path=relative,grader_url=f'https://github.com/EmbodiedSWE/EmbodiedSWE/blob/{code_rev}/{relative}',gallery=None,dataset_source=None) + if clip:task['gallery']=dict(path=f'media/{key}/website_demo.mp4',url=SITE+f"assets/video/{clip['f']}.mp4",website_playback_rate=clip['sp'],camera_label='WEBSITE RENDER VIEW') + keep_source(relative) + if key in REPOS: + directory=WORK/'metadata'/REPOS[key];d=read(directory/'discovery.json');episodes=read(directory/'episodes.json');info=d['info'];repo=d['repo_id'];revision=d['revision'];official_tasks=pq.read_table(directory/'tasks.parquet').to_pylist() + by_instruction={t.get('task',t.get('__index_level_0__')):t['task_index'] for t in official_tasks} + if key=='slice': + chosen=[] + for tid in [0,5,21]: + match=next(t for t in official_tasks if t['task_index']==tid);inst=match.get('task',match.get('__index_level_0__'));selected=next(e for e in episodes if e['tasks']==[inst]);chosen.append(selected) + elif len(official_tasks)>=3: + chosen=[next(e for e in episodes if e['tasks']==[t.get('task',t.get('__index_level_0__'))]) for t in [official_tasks[0],official_tasks[len(official_tasks)//2],official_tasks[-1]]] + else:chosen=[episodes[0],episodes[len(episodes)//2],episodes[-1]] + cameras=[k.removeprefix('observation.images.') for k,v in info['features'].items() if v['dtype']=='video'] + # Single-arm camera placement order: scene view(s), wrist. Never fake left/right labels. + cameras=sorted(cameras,key=lambda c:(c=='wrist',{'front':0,'front_wide':0,'high':1,'side':1,'corner':1}.get(c,2))) + task['dataset_source']=dict(repo_id=repo,revision=revision,format=info['codebase_version'],fps=info['fps'],total_episodes=info['total_episodes'],camera_order=cameras,task_index_scope='local to this source repository; not a benchmark-global task ID',robot_type=info['robot_type'],selected_episodes=[e['episode_index'] for e in chosen],source_license_metadata=d['license']) + for file in ['info.json','tasks.parquet','bake.json','modality.json']: + if (directory/file).exists(): + dst=OUT/'provenance/source_metadata'/key/file;dst.parent.mkdir(parents=True,exist_ok=True);shutil.copy2(directory/file,dst) + requested=[] + for e in chosen: + requested.append(info['data_path'].format(chunk_index=e['data/chunk_index'],file_index=e['data/file_index'])) + for c in cameras: + prefix=f'videos/observation.images.{c}' + requested.append(info['video_path'].format(video_key=f'observation.images.{c}',chunk_index=e[prefix+'/chunk_index'],file_index=e[prefix+'/file_index'])) + paths={x.path:x for x in api.get_paths_info(repo,sorted(set(requested)),repo_type='dataset',revision=revision)} + for ep in chosen: + eid=ep['episode_index'];inst=ep['tasks'][0];tid=by_instruction[inst];folder=f'media/{key}/episode_{eid:06d}';views=[] + for c in cameras: + prefix=f'videos/observation.images.{c}';p=info['video_path'].format(video_key=f'observation.images.{c}',chunk_index=ep[prefix+'/chunk_index'],file_index=ep[prefix+'/file_index']);obj=paths[p] + views.append(dict(camera=c,label=c.upper().replace('_',' '),path=f'{folder}/{c}.mp4',source_path=p,source_bytes=obj.size,source_sha256=obj.lfs.sha256 if obj.lfs else None,from_timestamp=ep[prefix+'/from_timestamp'],to_timestamp=ep[prefix+'/to_timestamp'])) + data_path=info['data_path'].format(chunk_index=ep['data/chunk_index'],file_index=ep['data/file_index']);obj=paths[data_path] + samples.append(dict(catalog_number=number,scene_key=key,source_repo=repo,source_revision=revision,task_index=tid,episode_index=eid,frame_count=ep['length'],fps=info['fps'],instruction=inst,views=views,trajectory_source_path=data_path,trajectory_source_bytes=obj.size,trajectory_path=f'trajectories/{key}/episode_{eid:06d}.parquet',composite_path=f'{folder}/synchronized.mp4',preview_path=f'{folder}/preview.jpg',metadata=ep)) + print(key,'selected IDs',[e['episode_index'] for e in chosen],'cameras',cameras,flush=True) + tasks.append(task) + assert len(tasks)==28 and len(samples)==30 + for path in ['LICENSE','README.md','robobench/README.md','robobench/robots/franka.py','robobench/core/robot.py','robobench/core/grader.py','vla/README.md','vla/convert/README.md']:keep_source(path) + shutil.copy2(CODE/'LICENSE',OUT/'LICENSE') + write(OUT/'provenance/tasks.json',tasks);write(OUT/'provenance/samples.json',samples) + write(OUT/'provenance/source.json',dict(benchmark='EmbodiedSWE',code_repository='EmbodiedSWE/EmbodiedSWE',code_revision=code_rev,project_url=SITE+'#benchmark',retrieved_date='2026-10-05',catalog_number_scope='C01–C28 are this browser’s auxiliary numbers in website catalog order; use suite.scene as the official task identifier.',task_index_scope='Official LeRobot task_index and episode_index are LOCAL TO EACH SOURCE REPOSITORY. Keep source_repo to disambiguate.',sample_policy='Three complete selected episodes per dataset-backed scene; instruction/food diversity where source has multiple tasks, otherwise first/middle/last.',scoring_note='Current public grader source may differ from the historical data-generation graders. Do not infer actual scores from current rubric or clip appearance.',camera_note='Single-arm datasets have front/front_wide/high/side/corner and one wrist camera, depending on scene. Website demos expose only their recorded view; missing views are not invented.',total_scenes=28,website_demos=26,source_datasets=10,selected_episodes=30,franka_wrist_camera={'parent_link':'panda_hand','eye_m':[0.0465,-.02,.036],'target_m':[.0465,-.02,.3],'focal_mm':10.5,'coordinate_frame':'parent link local frame, not world frame','virtual_mount_note':'This is a simulated optical extrinsic; it is not a physical screw-hole measurement.'})) + +def demos(): + tasks=read(OUT/'provenance/tasks.json') + def run(t): + d=t['gallery'] + if not d:return + dst=OUT/d['path'];dst.parent.mkdir(parents=True,exist_ok=True) + if not dst.exists(): + r=SESSION.get(d['url'],timeout=90);r.raise_for_status();dst.write_bytes(r.content) + d['sha256']=hashlib.sha256(dst.read_bytes()).hexdigest();d['bytes']=dst.stat().st_size + with cf.ThreadPoolExecutor(max_workers=5) as ex:list(ex.map(run,tasks)) + write(OUT/'provenance/tasks.json',tasks);print('Saved 26 official website MP4 clips, total',sum(t['gallery']['bytes'] for t in tasks if t['gallery']),flush=True) + +def encode(frames,path,fps): + import av + path.parent.mkdir(parents=True,exist_ok=True) + with av.open(str(path),'w',options={'movflags':'+faststart'}) as output: + stream=None;count=0 + for f in frames: + if stream is None: + stream=output.add_stream('libx264',rate=fps);stream.width=f.width;stream.height=f.height;stream.pix_fmt='yuv420p';stream.options={'crf':'22','preset':'veryfast','threads':'2'} + f=f.reformat(format='yuv420p');f.pts=count;f.time_base=Fraction(1,fps) + for packet in stream.encode(f):output.mux(packet) + count+=1 + if stream is None:raise ValueError('No source frames') + for packet in stream.encode():output.mux(packet) + return count + +def extract(): + import av + samples=read(OUT/'provenance/samples.json');jobs=collections.defaultdict(list) + report_path=OUT/'provenance/download_report.json' + summary=read(report_path).get('files',[]) if report_path.exists() else [] + for s in samples: + jobs[(s['source_repo'],s['source_revision'],s['trajectory_source_path'],'data')].append(s) + for v in s['views']:jobs[(s['source_repo'],s['source_revision'],v['source_path'],'video')].append((s,v)) + def run(key,entries): + repo,rev,path,kind=key + size=entries[0]['trajectory_source_bytes'] if kind=='data' else entries[0][1]['source_bytes'] + reader=HTTPRangeFile(source_url(repo,rev,path),size) + if kind=='data': + if all((OUT/s['trajectory_path']).exists() for s in entries):return None + file=pq.ParquetFile(reader) + # Episode-index statistics allow whole unused row groups to be skipped. + for s in entries: + target=OUT/s['trajectory_path'] + if target.exists():continue + table=pq.read_table(reader,filters=[('episode_index','=',s['episode_index'])]) + assert table.num_rows==s['frame_count'],(s['scene_key'],s['episode_index'],table.num_rows,s['frame_count']) + assert set(table['task_index'].to_pylist())=={s['task_index']} + target.parent.mkdir(parents=True,exist_ok=True);pq.write_table(table,target) + print('Trajectory',s['scene_key'],s['episode_index'],table.num_rows,flush=True) + else: + if all((OUT/v['path']).exists() for s,v in entries):return None + with av.open(reader) as container: + stream=container.streams.video[0] + for s,v in sorted(entries,key=lambda x:x[1]['from_timestamp']): + target=OUT/v['path'] + if target.exists(): + v['output_sha256']=hashlib.sha256(target.read_bytes()).hexdigest() + continue + start=v['from_timestamp'];fps=s['fps'];container.seek(int(start/stream.time_base),stream=stream,backward=True) + def frames(): + count=0 + for f in container.decode(stream): + if f.time+1e-5=s['frame_count']:break + expected=start+count/fps + assert abs(f.time-expected)<1/fps*.55,(s['scene_key'],s['episode_index'],v['camera'],f.time,expected) + yield f;count+=1 + assert count==s['frame_count'],(s['scene_key'],s['episode_index'],count,s['frame_count']) + count=encode(frames(),target,fps);v['output_sha256']=hashlib.sha256(target.read_bytes()).hexdigest() + print('Video',s['scene_key'],s['episode_index'],v['camera'],count,'frames',flush=True) + return dict(repo_id=repo,revision=rev,path=path,kind=kind,full_source_bytes=size,received_range_bytes=reader.transferred) + with cf.ThreadPoolExecutor(max_workers=4) as ex: + futures={ex.submit(run,k,e):k for k,e in jobs.items()} + for f in cf.as_completed(futures): + result=f.result() + if result:summary.append(result) + write(OUT/'provenance/samples.json',samples) + write(OUT/'provenance/download_report.json',dict(whole_video_shards_downloaded=False,assets_downloaded=False,checkpoints_downloaded=False,range_bytes_received=sum(x['received_range_bytes'] for x in summary),byte_count_scope='Completed source-group reads only; incomplete reads interrupted during resume may add uncounted overhead.',files=summary)) + print('Selective range bytes:',sum(x['received_range_bytes'] for x in summary),flush=True) + +def render(): + from PIL import Image,ImageDraw + samples=read(OUT/'provenance/samples.json') + def run(s): + n=len(s['views']);fps=s['fps'];target=OUT/s['composite_path'] + # Allows rendering to overlap selective extraction without ever reading + # an unfinished MP4 (its moov/nb_frames only exist after the writer closes). + deadline=time.monotonic()+1800 + while True: + complete=True + for v in s['views']: + p=OUT/v['path'] + if not p.exists():complete=False;break + r=subprocess.run(['ffprobe','-v','error','-select_streams','v:0','-show_entries','stream=nb_frames','-of','json',str(p)],capture_output=True,text=True) + try:valid=r.returncode==0 and int(json.loads(r.stdout)['streams'][0]['nb_frames'])==s['frame_count'] + except (ValueError,KeyError,IndexError):valid=False + if not valid:complete=False;break + if complete:break + if time.monotonic()>deadline:raise TimeoutError(f"Source cameras not finished: {s['scene_key']} {s['episode_index']}") + time.sleep(2) + if not target.exists(): + labels=Image.new('RGB',(320*n,28),'#14251e');draw=ImageDraw.Draw(labels) + for i,v in enumerate(s['views']):draw.text((i*320+10,8),v['label'],fill='white') + label_path=WORK/f"labels-{s['scene_key']}-{s['episode_index']}.png";labels.save(label_path) + cmd=['ffmpeg','-v','error','-y'] + for v in s['views']:cmd+=['-i',str(OUT/v['path'])] + cmd+=['-loop','1','-i',str(label_path)] + filters=';'.join(f'[{i}:v]scale=320:240,setsar=1[c{i}]' for i in range(n))+';'+''.join(f'[c{i}]' for i in range(n))+f'hstack=inputs={n}[v];[{n}:v]format=yuv420p[l];[l][v]vstack=inputs=2:shortest=1[out]' + cmd+=['-filter_complex',filters,'-map','[out]','-c:v','libx264','-preset','veryfast','-crf','23','-pix_fmt','yuv420p','-threads','2','-frames:v',str(s['frame_count']),'-r',str(fps),'-movflags','+faststart',str(target)];subprocess.run(cmd,check=True,capture_output=True) + if not (OUT/s['preview_path']).exists():subprocess.run(['ffmpeg','-v','error','-y','-i',str(target),'-frames:v','1','-q:v','2',str(OUT/s['preview_path'])],check=True,capture_output=True) + print('Rendered',s['scene_key'],s['episode_index'],flush=True) + with cf.ThreadPoolExecutor(max_workers=3) as ex:list(ex.map(run,samples)) + checks=[] + for s in samples: + data=pq.read_table(OUT/s['trajectory_path']);assert data.num_rows==s['frame_count'];assert set(data['task_index'].to_pylist())=={s['task_index']};assert set(data['episode_index'].to_pylist())=={s['episode_index']} + videos=[] + for path in [v['path'] for v in s['views']]+[s['composite_path']]: + stat=json.loads(subprocess.check_output(['ffprobe','-v','error','-select_streams','v:0','-show_entries','stream=codec_name,width,height,nb_frames,duration,avg_frame_rate','-of','json',str(OUT/path)],text=True))['streams'][0] + assert stat['codec_name']=='h264' and int(stat['nb_frames'])==s['frame_count'],(path,stat) + assert abs(float(Fraction(stat['avg_frame_rate']))-s['fps'])<.001 + videos.append(dict(path=path,**stat)) + checks.append(dict(scene_key=s['scene_key'],source_repo=s['source_repo'],task_index=s['task_index'],episode_index=s['episode_index'],videos=videos)) + write(OUT/'provenance/validation.json',dict(passed=True,selected_episodes=30,official_task_scenes=28,website_demos=26,checks=checks));print('All 30 episode IDs, source task IDs, frame counts and FPS verified.',flush=True) + +def package(): + import yaml + from datasets import Dataset,Features,Value,Video + tasks=read(OUT/'provenance/tasks.json');samples=read(OUT/'provenance/samples.json');source=read(OUT/'provenance/source.json');rows=[] + for t in tasks: + common=dict(catalog_number=t['catalog_number'],scene_key=t['scene_key'],official_scene_id=t['official_scene_id'],task_name=t['name'],category=t['category'],difficulty=t['difficulty'],benchmark_instruction=t['instruction'],rubric=json.dumps(t['rubric']),grading_description=t['grader_description'],success_source=t['success_source'],grader_url=t['grader_url'],actual_episode_score=None,actual_episode_success=None) + for s in [s for s in samples if s['scene_key']==t['scene_key']]: + camera_dict={v['camera']:dict(path=media_path(v['path']),bytes=None) for v in s['views']} + row=dict(common,row_kind='official_dataset_episode',source_repo=s['source_repo'],source_revision=s['source_revision'],task_index=s['task_index'],episode_index=s['episode_index'],instruction=s['instruction'],synchronized=dict(path=media_path(s['composite_path']),bytes=None),view_1=camera_dict[s['views'][0]['camera']],view_1_name=s['views'][0]['camera'],view_2=camera_dict[s['views'][1]['camera']],view_2_name=s['views'][1]['camera'],view_3=camera_dict[s['views'][2]['camera']] if len(s['views'])>2 else None,view_3_name=s['views'][2]['camera'] if len(s['views'])>2 else '',website_demo=None,frame_count=s['frame_count'],fps=s['fps'],duration_seconds=s['frame_count']/s['fps'],trajectory_url=f'https://huggingface.co/datasets/{DEST}/resolve/main/'+s['trajectory_path'],notes='Source task_index / episode_index are local to source_repo. H.264 transcode keeps every selected source frame at its original dataset FPS; any upstream idle filtering remains part of the official data. Current public rubric is not a measured score for this episode.') + rows.append(row) + d=t['gallery'] + if d:rows.append(dict(common,row_kind='official_website_demo',source_repo='',source_revision='',task_index=None,episode_index=None,instruction=t['instruction'],synchronized=None,view_1=None,view_1_name='',view_2=None,view_2_name='',view_3=None,view_3_name='',website_demo=dict(path=media_path(d['path']),bytes=None),frame_count=None,fps=None,duration_seconds=None,trajectory_url='',notes=f"Official website clip: {d['url']}. Website playback rate {d['website_playback_rate']}x; this browser defaults to 1x of the published file. No paired camera/trajectory inferred.")) + if not d and not t['dataset_source']:rows.append(dict(common,row_kind='catalog_only_no_published_clip',source_repo='',source_revision='',task_index=None,episode_index=None,instruction=t['instruction'],synchronized=None,view_1=None,view_1_name='',view_2=None,view_2_name='',view_3=None,view_3_name='',website_demo=None,frame_count=None,fps=None,duration_seconds=None,trajectory_url='',notes='The official website currently has no demonstration clip, and no corresponding public HF demonstration dataset was located. Keep task objective and published grading source.')) + videos={'synchronized','view_1','view_2','view_3','website_demo'};ints={'catalog_number','task_index','episode_index','frame_count','fps'} + features=Features({k:Video(decode=False) if k in videos else Value('int64') if k in ints else Value('float64') if k in ['duration_seconds','actual_episode_score'] else Value('bool') if k=='actual_episode_success' else Value('string') for k in rows[0]}) + ds=Dataset.from_list(rows,features=features);(OUT/'data').mkdir(exist_ok=True);configs=[] + def subset(name,ids,default=False): + p=f'data/{name}.parquet';ds.select(ids).to_parquet(str(OUT/p),batch_size=32);c=dict(config_name=name,data_files=[dict(split='examples',path=p)]) + if default:c['default']=True + configs.append(c) + subset('all',list(range(len(rows))),True) + for t in tasks:subset('scene_'+t['scene_key'],[i for i,r in enumerate(rows) if r['scene_key']==t['scene_key']]) + for group in sorted({t['category'] for t in tasks}):subset(group.lower(),[i for i,r in enumerate(rows) if r['category']==group]) + header=dict(language=['en','zh'],license='other',license_name='upstream-embodiedswe-terms',license_link=f'https://huggingface.co/datasets/{DEST}/blob/main/NOTICE.md',pretty_name='EmbodiedSWE Simulation Task Examples',tags=['robotics','video','simulation'],configs=configs) + browser='https://travor278-embodiedswe-task-examples.static.hf.space' + lines=['---',yaml.safe_dump(header,allow_unicode=True,sort_keys=False).rstrip(),'---','# EmbodiedSWE Simulation Task Examples',f'\n[打开任务 MP4 浏览页]({browser}/?task=bulb)', '\n28 个官网场景 · 26 段官网 MP4 · 10 个官方数据来源各 3 条完整 episode，共 30 条多相机示教。shoe_knot 与 dice 暂无官方展示视频，保留任务目录和评分标准。', + '\n**序号规则：** 本页 C01–C28 是按官网目录顺序生成的辅助编号，官方任务以 `suite.scene` 标识。LeRobot `task_index` 和 `episode_index` 都只在各自 `source_repo` 内有效，绝不能把各库的 task_index=0 当成同一任务。', + '\n**相机：** 按官方实际提供的 front/front_wide/high/side/corner/wrist 展示，单臂 wrist 不伪装成 left/right。官网演示只展示其原有视角。', + '\n**评分：** 来自当前公开代码的 BaseGrader 与具体 RUBRIC，progress 是归一化权重和。live=当前值；once=历史最好；final=交付末端才评估。历史数据生成使用的 grader 可能不同；示例没有公开逐条分数，因此 actual_episode_score/success 留空。', + f'\n[Franka 腕部相机说明]({browser}/?task=camera-guide) · [官方任务页](https://embodiedswe.github.io/#benchmark)', + '\n| 辅助目录号 | 官方 scene ID | Task | Category | 直接浏览 |','|---|---|---|---|---|'] + for t in tasks:lines.append(f"| C{t['catalog_number']:02d} | `{t['official_scene_id']}` | {t['name']} | {t['category']} | [打开]({browser}/?task={t['scene_key']}) |") + for t in tasks: + lines.extend([f"\n## C{t['catalog_number']:02d} · {t['name']} · {t['official_scene_id']}",f"\n**Instruction / objective:** {t['instruction']}",f"\n**Difficulty:** {t['difficulty']} · **官网列出的 evaluated on:** {t['catalog_embodiments']}",f"\n[MP4 浏览]({browser}/?task={t['scene_key']}) · [官方评分源码]({t['grader_url']})",'\n| Stage | Weight | Normalized share | Mode |','|---|---|---|---|']) + lines.extend(f"| {s['stage']} | {s['weight']} | {s['normalized_weight']:.2%} | {s['mode']} |" for s in t['rubric']) + lines.extend(['\n```python',t['success_source'],'```']) + if t['dataset_source']:lines.append(f"\n**Official dataset:** [{t['dataset_source']['repo_id']}](https://huggingface.co/datasets/{t['dataset_source']['repo_id']}) · {t['dataset_source']['fps']} FPS · cameras: {', '.join(t['dataset_source']['camera_order'])}") + for s in [s for s in samples if s['scene_key']==t['scene_key']]: + lines.extend([f"\n**source task_index={s['task_index']} / episode_index={s['episode_index']}** · {s['frame_count']} frames / {s['fps']} FPS / {s['frame_count']/s['fps']:.2f} s",f"\n**Dataset instruction:** {s['instruction']}",f"\n![Source camera composite]({s['preview_path']})",f"\n[同步 MP4]({s['composite_path']}) · "+' · '.join(f"[{v['camera']} MP4]({v['path']})" for v in s['views'])+f" · [官方轨迹 Parquet]({s['trajectory_path']})"]) + if t['gallery']:lines.append(f"\n[官网展示 MP4]({t['gallery']['path']}) · 网站原始播放倍率 {t['gallery']['website_playback_rate']}x；该示例不等同于公开三相机训练轨迹。") + else:lines.append('\n官方目录中该任务目前没有公开展示 MP4。') + lines.extend(['\n## 来源与复现',f'\n代码 revision：`{source["code_revision"]}`。官网抓取日期：2026-10-05。','\n保留各数据源固定 revision、源文件大小与校验和、官方 episode 时间区间和元数据、源码评分说明。合并源视频采用 HTTP Range 读取选定 episode；不下载完整 assets、权重或整库。','\n脚本 `scripts/build_examples.py` 可基于保留的 manifests 运行 demos/extract/render/package；浏览页源码保留在 browser/ 与 scripts/build_browser.py。','\n官方代码采用 Apache-2.0；部分源数据未单列许可元数据。本整理保留各来源权利与署名，不另行扩张许可；见 NOTICE.md、LICENSE 和 reference/。']) + (OUT/'README.md').write_text('\n'.join(lines)+'\n',encoding='utf-8');(OUT/'scripts').mkdir(exist_ok=True) + (OUT/'NOTICE.md').write_text('# Source attribution and terms\n\nUnofficial curated examples. Official task code, descriptions and episodes are credited to the EmbodiedSWE authors.\n\nThe upstream code LICENSE is Apache-2.0 and is retained verbatim in LICENSE. Some official HF datasets declare Apache-2.0; others do not declare a dataset-specific license in their card metadata. Per-source metadata is preserved in provenance/tasks.json. Website clips are credited and linked to their original project-page files. This curation does not create additional rights or replace upstream terms.\n',encoding='utf-8') + if Path(__file__).resolve()!=(OUT/'scripts/build_examples.py').resolve():shutil.copy2(__file__,OUT/'scripts/build_examples.py') + print('Packaged',len(rows),'rows in',len(configs),'native HF subsets.',flush=True) + +if __name__=='__main__': + WORK.mkdir(exist_ok=True);(OUT/'provenance').mkdir(parents=True,exist_ok=True);globals()[sys.argv[1]]() diff --git a/trajectories/bulb/episode_000000.parquet b/trajectories/bulb/episode_000000.parquet new file mode 100644 index 0000000000000000000000000000000000000000..937aedf1d36264a9b4905b38af3cf813d07740cb --- /dev/null +++ b/trajectories/bulb/episode_000000.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6821bdab6ecdacf0f9e04458c2223585fca3522b2129fa24010254d4d8fa4662 +size 361652 diff --git a/trajectories/bulb/episode_000408.parquet b/trajectories/bulb/episode_000408.parquet new file mode 100644 index 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