yam-pick-duster-200 — joint space (LeRobot v3.0)

200 teleoperated demonstrations of a single I2RT YAM arm picking up a blue duster and placing it in a red box, recorded in VR. Joint-space state and action, two camera views.

An end-effector-space version of the same 200 takes is published separately as Dimios45/yam-pick-duster-200-ee — same episodes, same wall-clock spans, different action space and format. Both were written from one control loop, so episode N is the same demonstration in each.

At a glance

Episodes	200
Frames	76,307 @ 25 Hz (50.9 min)
Episode length	109–280 frames (10.8–27.9 s), median 14.5 s
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" (pick the duster, place it in the box)

Recorded in two sittings of 100 on 2026-08-17/18.

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 — the inverse of i2rt's native normalisation, 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.

Data quality

Measured over all 200 episodes:

• 86% of frames contain motion (range 64–94%). No idle or dead takes.
• Start pose repeatability: 0.08 / 0.33 / 0.11 mm std — every episode begins from the same home pose (the operator ramps the arm home between takes).
• Lighting is stable: top-camera brightness std 2.4/255, with per-50-episode medians of 144.4 / 143.8 / 144.0 / 144.1 — no drift across either sitting.
• 198/200 episodes contain a grasp. Two takes (20260818T004003534366, 20260818T005359521504) were aborted without closing the gripper; exclude them at conversion time if you want a clean set.
• 14 episodes re-grasp (two close events) — a failed first attempt followed by a retry. Useful recovery behaviour, but worth knowing it is in there.

Object placement

Both the duster and the box were repositioned between episodes, so neither is at a fixed location:

	x range	y range	std
grasp (duster pickup)	0.35–0.60 m	−0.15 to +0.25 m	52 / 119 mm
release (into the box)	0.32–0.60 m	−0.14 to +0.25 m	63 / 123 mm

Coverage is not uniform — the first 100 episodes concentrated in two bands with a gap around y ≈ +0.05, which the second 100 partly filled. Reach spans roughly 50 × 48 × 31 cm in x/y/z.

Recording setup

Meta Quest controllers → WebXR → differential IK → joint commands, using vr-teleop-kit. The 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.

Loading

from lerobot.datasets.lerobot_dataset import LeRobotDataset

ds = LeRobotDataset("Dimios45/yam-pick-duster-200")
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).

Training a B-spline diffusion policy on this

The bspline-policy stack reads robomimic HDF5. to_robomimic.py is included in this repo:

python to_robomimic.py --from lerobot \
    --repo-id Dimios45/yam-pick-duster-200 --root <local-root> \
    --output-path yam_joint.hdf5

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:

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]

With action.shape: [7], _convert_actions passes the joint targets through untouched — that is intended, not a bug.

Cropping

Frames are stored uncropped 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; the wrist camera sees the room above the table horizon, but that crop is pose-dependent (the horizon moves as the arm pitches), so verify across your workspace before committing.

Whatever crop you train with must be applied identically at deployment. The converter stamps the crop into the HDF5 attributes so the choice travels with the data.

Licence

Apache-2.0.