lerobot/so101-tabletop-mujoco

SO-101 tabletop (MuJoCo)

A LeRobot EnvHub environment: an SO-101 arm in the turquoise of the LeRobot kits, four painted wooden 3 cm cubes (red, green, blue, yellow) and a green open box on a table in a simple room, simulated with MuJoCo. Each reset places the cubes, the box and the front camera with seeded offsets.

An Isaac Sim version with the same layout, cameras, observations and actions is lerobot/so101-tabletop-isaac: the same policies and commands run against both.

Observation	pixels: front (scene camera) and wrist (gripper camera), 480×640 RGB; agent_pos: 6 joint positions
Action	6 absolute joint position targets: shoulder_pan, shoulder_lift, elbow_flex, wrist_flex, wrist_roll in degrees, gripper 0-100 (the units of LeRobot's so101_follower with use_degrees=True)
Control rate	30 fps
Task	"Put all the cubes in the green box."
Reward	the fraction of cubes in the box at each step (0, 0.25, ... 1); info["cubes_in_box"] gives the count
Episode end	success (terminated) when all four cubes are in the box; truncated after 3600 steps (2 minutes), set with episode_length
Reset	seeded cube and box poses, front camera jitter, joint noise

With the LeRobot simulator server

From a LeRobot checkout, start the server. The image installs requirements.txt, and env.py declares the control rate and joint names in LEROBOT_SIM:

lerobot-env-server --docker --envhub=lerobot/so101-tabletop-mujoco

Then run a policy against it, for example:

lerobot-rollout --robot.type=remote --robot.endpoint=tcp/127.0.0.1:7448 \
  --robot.deployment=envhub --robot.task=so101_tabletop \
  --policy.path=<your SO-101 checkpoint> --fps=30

Observations reach policies as observation.images.front, observation.images.wrist and observation.state; use --rename_map if your checkpoint names its cameras differently.

In-process

from lerobot.envs.factory import make_env

envs = make_env("lerobot/so101-tabletop-mujoco", n_envs=1, trust_remote_code=True)

make_env keyword arguments override the defaults of SO101Env in env.py (episode length, image size, cameras, initial joints, reset randomization). Headless machines need MUJOCO_GL=egl.

Files

File	Content
env.py	the EnvHub entry point (make_env), the Gymnasium env and LEROBOT_SIM
so101_tabletop.xml	the scene: room, table, cubes, box, lights, front camera and the arm's mount site
robotstudio_so101/	MuJoCo Menagerie's SO-101 model, attached at the mount site (env.py recolors its printed parts)
textures/wood.png	a seamless wood grain, tinted per cube

Licenses

• This environment is Apache-2.0, including textures/wood.png (generated procedurally for it).
• robotstudio_so101/ is MuJoCo Menagerie's SO-101 model (commit 0059d4335f8156206f63a35662313385f7ad6d74), derived from TheRobotStudio's SO-ARM100, under the Apache-2.0 license in that folder.