Datasets:

dobri420
/

pick-cube-so101-sim

license: apache-2.0
task_categories:
  - robotics
tags:
  - LeRobot
  - robotics
  - lerobot
  - so101
  - mujoco
  - sim
  - pick-cube
pretty_name: Pick-Cube SO-101 (MuJoCo sim twin)
configs:
  - config_name: default
    data_files: data/*/*.parquet

Pick-Cube SO-101 — MuJoCo sim twin

A 1-to-1 MuJoCo reproduction of the real teleoperated dobri420/pick-cube-so101 dataset. Every real episode is replayed through a MuJoCo sim of the SO-101 cell and re-rendered from the same three camera views — see the source card for the full breakdown of tranches, grasp distribution, and recording setup.

Generator code: dyordan1/so101-mujoco.

How it was made

Proprioception is verbatim. action and observation.state (6-DoF joint positions) are copied from the real dataset unchanged — only the pixels are synthetic.
Pixels are MuJoCo. The three views (camera1/camera2/camera3, 480×640) are re-rendered in sim, with the cube welded at the recorded grasp frame, the tote at the release frame, and each episode's distractors placed in the fan.
Sim-fidelity filter. Each episode is first rolled physics-only to check the cube actually lands in the tote; episodes that don't land in sim are dropped, and the survivors renumbered. So this twin has 535 episodes (vs the source's
1. — the ones the sim faithfully reproduces.

Train and test your own policy — no robot needed

Because both the data and the environment are in the loop, you can train a policy on this dataset and evaluate it in the same MuJoCo sim it came from — with no physical arm and no need to reproduce the real scene lighting/geometry. The generator repo dyordan1/so101-mujoco ships the whole loop:

git clone https://github.com/dyordan1/so101-mujoco && cd so101-mujoco
pip install -r requirements.txt

python download.py                       # this dataset -> datasets/
scripts/train                            # finetune SmolVLA -> checkpoints/  (needs a GPU)
python mujoco_policy.py \
  checkpoints/pick-cube-so101-sim-smolvla/checkpoints/last/pretrained_model --grid

scripts/train wraps lerobot-train with the SmolVLA recipe; mujoco_policy.py places the cube in the sim and lets the policy drive (--reach/--azim to move the cube, --view for the 3D viewer). Same loop works on the real dobri420/pick-cube-so101.

Usage

from lerobot.datasets.lerobot_dataset import LeRobotDataset

ds = LeRobotDataset("dobri420/pick-cube-so101-sim")
print(ds.num_episodes, ds.num_frames)  # 535 205329

Built with LeRobot (codebase_version: v3.0).