README.md

metadata

license: apache-2.0
task_categories:
  - robotics
tags:
  - LeRobot
  - so101
  - pick-place
  - manipulation
pretty_name: Pick-Cube SO-101
configs:
  - config_name: default
    data_files: data/*/*.parquet

Pick-Cube SO-101

A deliberately structured teleoperated pick-and-place dataset on the SO-101 arm — 540 episodes of "grab the cube and put it in the box", structured to systematically vary grasp coverage, reach depth, wrist angle, and scene clutter.

MuJoCo sim twin: dobri420/pick-cube-so101-sim — a 1-to-1 re-render of these episodes in simulation (same proprioception, synthetic pixels).

Trained model

A SmolVLA policy finetuned on this dataset (first 340 episodes, through the twist-CCW tranche) reaches 89% grab rate (72/81) on the real arm — balanced across reach depth: dobri420/pick-cube-smolvla-so101 (model). A further checkpoint trained on the full 540 episodes (2.56M samples, batch 96) is pending real-arm evaluation.

Composition

Seven tranches, recorded in order:

Episodes	Tranche	What
0–49	discrete @ 25 cm	5 target angles (0°, ±45°, ±90°) × 10 demos
50–69	dense jitter	20 demos clustered at 45° right, ±2 cm placement jitter
70–119	discrete @ 15 cm	the same 5 angles at the near depth × 10 demos
120–239	blanket jitter	120 demos spread across the workspace
240–339	twist-CCW jitter	100 demos with a counter-clockwise–twisted wrist for grasp-angle variability
340–439	twist-CW jitter	100 demos with a clockwise–twisted wrist — the mirror of the CCW tranche
440–539	distractor jitter	100 blanket-style demos with 1–3 distractor objects on the workspace

Grasp distribution

Each arrow is one episode's grasp — its position on the workspace half-disk (rings = reach in cm from the shoulder-pan axis, radials = target angle) and its wrist yaw (arrowhead points toward the upper jaw), recovered by SO-101 forward kinematics on the jaw center. Red arrows highlight the named tranche within the full dataset; a stamped digit is that episode's distractor count. The grid is calibrated from this dataset's own registration clusters.

The discrete tranches sit as tight clusters on the 15/25 cm rings; blanket jitter fills the gaps; the twist tranches visibly rotate the arrows off-radial (counter-clockwise, then clockwise) — the wrist variability the earlier tranches lack; distractor jitter repeats the blanket spread with cluttered scenes (the stamped counts), so the policy must find the cube, not the object.

Recording

• Robot: SO-101 leader→follower pair, teleoperated at 30 fps.
• Cameras: three views — camera1 top, camera2 wrist, camera3 side (480×640).
• State / action: 6-DoF joint positions (shoulder_pan … gripper), in degrees.
• Task: "Grab the cube and put it in the box."

Usage

from lerobot.datasets.lerobot_dataset import LeRobotDataset

ds = LeRobotDataset("dobri420/pick-cube-so101")
print(ds.num_episodes, ds.num_frames)  # 540 ...

Built with LeRobot (codebase_version: v3.0).