Document schema, recording setup, conversion and cropping

README.md

@@ -3,186 +3,123 @@ license: apache-2.0
 task_categories:
 - robotics
 tags:
-- LeRobot
 - robotics
 - lerobot
 - yam
 - teleoperation
+- imitation-learning
 - manipulation
-configs:
-- config_name: default
-  data_files: data/*/*.parquet
 ---
 
-This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
-
-
-<a class="flex" href="https://huggingface.co/spaces/lerobot/visualize_dataset?path=Dimios45/yam-pick-duster">
-<img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl.svg"/>
-<img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl-dark.svg"/>
-</a>
-
-
-## Dataset Description
-
-
-
-- **Homepage:** [More Information Needed]
-- **Paper:** [More Information Needed]
-- **License:** apache-2.0
-
-## Dataset Structure
-
-[meta/info.json](meta/info.json):
-```json
-{
-    "codebase_version": "v3.0",
-    "fps": 25,
-    "features": {
-        "observation.state": {
-            "dtype": "float32",
-            "shape": [
-                7
-            ],
-            "names": [
-                "right_joint_1",
-                "right_joint_2",
-                "right_joint_3",
-                "right_joint_4",
-                "right_joint_5",
-                "right_joint_6",
-                "right_gripper"
-            ]
-        },
-        "action": {
-            "dtype": "float32",
-            "shape": [
-                7
-            ],
-            "names": [
-                "right_joint_1",
-                "right_joint_2",
-                "right_joint_3",
-                "right_joint_4",
-                "right_joint_5",
-                "right_joint_6",
-                "right_gripper"
-            ]
-        },
-        "observation.images.top": {
-            "dtype": "video",
-            "shape": [
-                480,
-                640,
-                3
-            ],
-            "names": [
-                "height",
-                "width",
-                "channels"
-            ],
-            "info": {
-                "video.height": 480,
-                "video.width": 640,
-                "video.codec": "av1",
-                "video.pix_fmt": "yuv420p",
-                "video.fps": 25,
-                "video.channels": 3,
-                "has_audio": false,
-                "video.g": 2,
-                "video.crf": 30,
-                "video.preset": 12,
-                "video.fast_decode": 0,
-                "video.video_backend": "pyav",
-                "video.extra_options": {},
-                "is_depth_map": false
-            }
-        },
-        "observation.images.right_wrist": {
-            "dtype": "video",
-            "shape": [
-                480,
-                640,
-                3
-            ],
-            "names": [
-                "height",
-                "width",
-                "channels"
-            ],
-            "info": {
-                "video.height": 480,
-                "video.width": 640,
-                "video.codec": "av1",
-                "video.pix_fmt": "yuv420p",
-                "video.fps": 25,
-                "video.channels": 3,
-                "has_audio": false,
-                "video.g": 2,
-                "video.crf": 30,
-                "video.preset": 12,
-                "video.fast_decode": 0,
-                "video.video_backend": "pyav",
-                "video.extra_options": {},
-                "is_depth_map": false
-            }
-        },
-        "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
-        }
-    },
-    "total_episodes": 50,
-    "total_frames": 28068,
-    "total_tasks": 1,
-    "chunks_size": 1000,
-    "data_files_size_in_mb": 100,
-    "video_files_size_in_mb": 200,
-    "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": "yam",
-    "splits": {
-        "train": "0:50"
-    }
-}
+# yam-pick-duster — joint-space (LeRobot v3.0)
+
+50 teleoperated demonstrations of a single I2RT YAM arm picking up a duster,
+recorded in VR. **Joint-space** state and action, two camera views.
+
+An end-effector-space version of the *same 50 takes* is published separately as
+[`Dimios45/yam-pick-duster-ee`](https://huggingface.co/datasets/Dimios45/yam-pick-duster-ee) —
+same episodes, same wall-clock spans, different action space and format.
+
+## At a glance
+
+| | |
+| --- | --- |
+| Episodes | 50 |
+| Frames | 28,068 @ 25 Hz (18.7 min) |
+| Episode length | 396–785 frames (15.8–31.4 s), median 547 |
+| Robot | I2RT YAM, 6-DoF + `linear_4310` gripper, right arm only |
+| Cameras | `top` (fixed overhead), `right_wrist` — both 640×480 RGB, **uncropped** |
+| Task | `"pick up the duster"` |
+
+Roughly 88% of frames contain motion (min 74%, max 92%) — there are no idle or
+dead takes in this set.
+
+## Schema
+
 ```
+observation.state  float32 (7,)  [right_joint_1..6 (rad), right_gripper]   measured
+action             float32 (7,)  same layout                               commanded
+observation.images.top          video (480, 640, 3)
+observation.images.right_wrist  video (480, 640, 3)
+```
+
+**Gripper convention: 0 = open, 1 = closed.** This is the inverse of i2rt's
+native normalisation, which is converted at record time.
+
+`action` is what the teleoperator commanded on that tick; `observation.state`
+is what the arm measured. The command leads the measurement by a few ticks, as
+expected of a position-controlled arm under load.
+
+## How it was recorded
+
+Meta Quest controllers → WebXR → differential IK → joint commands, using
+[vr-teleop-kit](https://github.com/Dream-Machines-Robotics/vr-teleop-kit).
+Operator holds a grip button to clutch the arm; the trigger drives the gripper.
+
+The arm is commanded at **200 Hz** and the dataset is *sampled* from that loop
+at 25 Hz. Rate matters: an earlier version commanded at the dataset rate and
+the arm was visibly jittery, because it received a new joint target only every
+1/fps s and the IK's per-tick velocity cap tightened by the same factor. Each
+episode begins from the same home pose (start poses agree to 0.30 mm across all
+50 takes).
+
+## Loading
+
+```python
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+ds = LeRobotDataset("Dimios45/yam-pick-duster")
+item = ds[0]
+item["observation.state"]              # (7,) joints + gripper
+item["observation.images.top"]         # (3, 480, 640) float32 in [0, 1], RGB
+```
+
+Works directly with LeRobot-native policies (ACT, diffusion policy, pi0,
+SmolVLA) — nothing extra needed.
+
+## Training a B-spline diffusion policy on this
+
+The [bspline-policy](https://github.com/haoyu-x/simple_mobile_bsp) stack reads
+robomimic HDF5, not LeRobot, and dispatches on the observation keys. Convert
+with `tools/to_robomimic.py` from vr-teleop-kit:
+
+```bash
+python tools/to_robomimic.py --from lerobot \
+    --repo-id Dimios45/yam-pick-duster --root <local-root> \
+    --output-path yam_joint.hdf5 \
+    --crop top_image=42,28,598,414        # optional, see below
+```
+
+That yields `obs/joint_pos (N,7)`, `obs/top_image`, `obs/wrist_image`
+(84×84 RGB) and `actions (N,7)` — the stack's `single_yam_joint` format, which
+needs no rotation conversion and **no IK at deployment**. Matching `shape_meta`:
+
+```yaml
+shape_meta: &shape_meta
+  obs:
+    top_image:   {shape: [3, 84, 84], type: rgb}
+    wrist_image: {shape: [3, 84, 84], type: rgb}
+    joint_pos:   {shape: [7]}
+  action:
+    shape: [7]
+```
+
+## Cropping
+
+Frames are stored **uncropped** on purpose, so the crop can be retuned without
+re-recording. The overhead camera's useful region is roughly
+`x=42, y=28, w=598, h=414` — this drops a corner artefact and the bench rail —
+but verify it against your own scene.
 
+The wrist camera sees the room above the table horizon (~y=110 at the home
+pose). That crop is **pose-dependent**: the horizon moves as the arm pitches,
+so a fixed rectangle that is clean at one pose can cut into the table at
+another. Check across your workspace before committing.
 
-## Citation
+**Whatever crop you train with must be applied identically at deployment**, or
+the policy sees an input distribution it never saw in training. The converter
+stamps the crop into the HDF5 attributes so the choice travels with the data.
 
-**BibTeX:**
+## Licence
 
-```bibtex
-[More Information Needed]
-```
+Apache-2.0.