grabette_pick3_cartesian_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

episodes	554
frames	91123
fps	50
duration	~30.4 min
camera	observation.images.cam0 at 480x360
codebase_version	v3.0

Channels

action (11D)

channels	meaning
dx, dy, dz	end-effector translation delta
dr6d_0 … dr6d_5	end-effector rotation delta, 6D rotation representation
proximal, distal	gripper joint angles, radians (0 = open, positive = closing)

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

task	episodes
pick up the mustard bottle	199
pick up the cup	189
pick up the red can	166

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.