| --- |
| license: apache-2.0 |
| task_categories: |
| - robotics |
| tags: |
| - robotics |
| - lerobot |
| - yam |
| - teleoperation |
| - imitation-learning |
| - manipulation |
| --- |
| |
| # 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. |
|
|
| **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. |
|
|
| ## Licence |
|
|
| Apache-2.0. |
|
|
