Stage 11-task VLA dataset release

README.md

@@ -1,3 +1,156 @@
 ---
 license: mit
+pretty_name: Genesis HR Bench VLA Demonstrations
+task_categories:
+- robotics
+tags:
+- robotics
+- manipulation
+- human-robot-interaction
+- imitation-learning
+- vision-language-action
+- genesis
+- franka
+size_categories:
+- 1K<n<10K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: manifest.jsonl
 ---
+
+# Genesis HR Bench VLA Demonstrations
+
+Scripted-expert demonstrations for human-robot collaborative manipulation in
+the [Genesis](https://github.com/Genesis-Embodied-AI/Genesis) simulator. This
+release contains exactly **300 validated successful episodes for each of 11
+tasks**: 3,300 episodes and 984,797 policy steps in total.
+
+The robot is a single-arm Franka. Observations include head-camera and
+right-wrist RGB-D streams, robot state, end-effector pose, and gripper state.
+Actions include absolute joint targets, joint deltas, end-effector deltas, and
+gripper targets. Collection uses 500 Hz simulation with a capture stride of 25,
+giving a 20 Hz policy rate.
+
+## Tasks
+
+- `categorize_cooperative`
+- `categorize_interrupt`
+- `deliver_to_human_easy`
+- `dump_bin_assist`
+- `dump_bin_interrupt`
+- `place_bread_in_basket_interrupt`
+- `place_burger_fries_interrupt`
+- `place_dual_shoes_interrupt`
+- `place_food_in_skillet_assist`
+- `pour_water`
+- `stack_bowls_three_interrupt`
+
+Additional completed tasks will be added in a later release.
+
+## Layout
+
+```text
+<task>/rasterizer/seed_<N>/
+├── meta.json
+├── steps.h5
+└── verdict.json
+```
+
+Each episode contains only these three regular files. There are no symlinks,
+failed attempts, in-flight episodes, debug logs, or surplus episodes.
+
+Repository-level metadata:
+
+- `manifest.jsonl`: one row per episode, used as the Hub viewer index
+- `dataset_summary.json`: selection policy and per-task inventory
+- `task_pool.txt`: task names in this release
+
+## `steps.h5` schema
+
+The HDF5 attribute `schema_version` is `vla_steps_v2_qpos_target`; `T` is the
+number of policy steps in an episode.
+
+| Key | Shape | Dtype | Description |
+|---|---:|---|---|
+| `images/head_camera` | `(T,)` | bytes | JPEG-encoded 480×640 RGB |
+| `images/right_wrist` | `(T,)` | bytes | JPEG-encoded 480×640 RGB |
+| `depth/head_camera` | `(T,480,640)` | float16 | Depth in metres |
+| `depth/right_wrist` | `(T,480,640)` | float16 | Depth in metres |
+| `qpos` | `(T,7)` | float32 | Measured arm joint positions |
+| `qpos_target` | `(T,7)` | float32 | Arm PD targets |
+| `ee` | `(T,7)` | float32 | End-effector `[x,y,z,qw,qx,qy,qz]` |
+| `gripper_meas` | `(T,)` | float32 | Measured gripper state |
+| `gripper_target` | `(T,)` | float32 | Target gripper state |
+| `qpos_target_action` | `(T,7)` | float32 | Absolute joint-target action |
+| `qpos_delta_action` | `(T,7)` | float32 | Joint-delta action |
+| `ee_delta_action` | `(T,6)` | float32 | End-effector delta action |
+| `gripper_target_action` | `(T,)` | float32 | Absolute gripper action |
+| `gripper_action` | `(T,)` | float32 | Gripper delta/action label |
+| `t` | `(T,)` | int64 | Simulation step index |
+
+The boolean `qpos_target_valid` and `qpos_target_action_valid` arrays mark
+valid labels. Quaternions use `(w, x, y, z)` ordering.
+
+`meta.json` contains the task instruction, seed, camera calibration, complete
+configuration snapshot, success result, and safety metrics. `verdict.json`
+contains the validation decision, success/plan status, error fields, and safety
+evaluation.
+
+## Loading one episode
+
+```python
+from huggingface_hub import snapshot_download
+import h5py
+import io
+import json
+import numpy as np
+from PIL import Image
+
+root = snapshot_download(
+    "zhouqh/hr_v2",
+    repo_type="dataset",
+    allow_patterns=["pour_water/rasterizer/seed_0/*"],
+)
+episode = f"{root}/pour_water/rasterizer/seed_0"
+
+with open(f"{episode}/meta.json") as f:
+    meta = json.load(f)
+
+with h5py.File(f"{episode}/steps.h5") as h:
+    qpos = h["qpos"][:]
+    rgb = np.stack([
+        np.asarray(Image.open(io.BytesIO(bytes(frame))))
+        for frame in h["images/head_camera"]
+    ])
+
+print(meta["instruction"], qpos.shape, rgb.shape)
+```
+
+Use `allow_patterns=["<task>/**"]` to download one task without downloading
+the complete dataset.
+
+## Selection and provenance
+
+Episodes were collected with the Genesis rasterizer backend and selected from
+validated successful rollouts. For each task, the first 300 successes in
+ascending numeric-seed order were retained. `dataset_summary.json` records the
+selected seed range and candidate inventory, and `manifest.jsonl` records each
+episode's task, seed, step count, cameras, schema, size, and source collection.
+
+## Limitations
+
+- This release contains successful scripted-expert demonstrations only; it is
+  not an unbiased sample of expert success or failure rates.
+- All observations are synthetic simulator outputs. Sim-to-real transfer is
+  not evaluated by this dataset.
+- HDF5 trajectory files are the canonical data. The Hub viewer displays the
+  episode-level `manifest.jsonl` index rather than decoding trajectory frames.
+- Safety metrics reflect the configured simulator geometry and checks recorded
+  during collection.
+
+## License and citation
+
+The dataset is released under the MIT license. If you use it, please cite the
+Genesis HR Bench project and this dataset repository.

categorize_cooperative/rasterizer/seed_0/meta.json

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+{
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+}

categorize_cooperative/rasterizer/seed_0/steps.h5

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categorize_cooperative/rasterizer/seed_0/verdict.json

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categorize_cooperative/rasterizer/seed_10/meta.json

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categorize_cooperative/rasterizer/seed_106/meta.json

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categorize_cooperative/rasterizer/seed_106/steps.h5

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categorize_cooperative/rasterizer/seed_106/verdict.json

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categorize_cooperative/rasterizer/seed_107/meta.json

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categorize_cooperative/rasterizer/seed_108/meta.json

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categorize_cooperative/rasterizer/seed_112/meta.json

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categorize_cooperative/rasterizer/seed_112/verdict.json

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categorize_cooperative/rasterizer/seed_114/meta.json

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categorize_cooperative/rasterizer/seed_118/meta.json

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