SteveNguyen/grabette_pick3_cartesian_480

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grabettepick3cartesian_480

Raw teleoperation recording from the grabette handheld device: SLAM-tracked end-effector motion plus the two gripper joint angles, as demonstrated. Nothing here is post-processed into a policy-specific representation.

At a glance

Channels

action (11D)

observation.state (2D): proximal, distal — the gripper only. The end-effector pose is deliberately absent: it is SLAM-frame-dependent, so feeding it to a policy ties the model to one recording session's origin.

is_lost flags frames where SLAM tracking was lost. Filter or reject those episodes before training — the pose deltas are meaningless there.

Tasks

The gripper channels are RAW angles

A position-controlled servo replaying a demonstrated gripper angle under-closes: the recorded angle is where the human's fingers sat while pressing the object, so reproducing it stops just short and grips nothing. Across these recordings the demonstrations use only 38–60% of the proximal range.

Train on this dataset directly and the policy inherits that problem. The fix is to re-express the two angles as a grasp shape plus a closure that can be commanded to 1.0 — "close all the way" — letting the object stop the fingers. See `docs/grasp_projection.md` and grabette_postprocess.grasp_projection_convert.

Loading

from lerobot.datasets import LeRobotDataset

ds = LeRobotDataset("SteveNguyen/grabette_pick3_cartesian_480")

Resolved by the git tag matching codebase_version (v3.0), not by main.