---
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.