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AshDash93/
toy-sorting-env

Name: toy-sorting-env
Availability: InStock

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SO-ARM 101 Toy Sorting — Isaac Lab EnvHub

Simulation-first manipulation learning with Isaac Lab + LeRobot. SO-ARM 101 sorts colored toys into matching trays on a wooden kitchen table.

LeRobot EnvHub

This repo follows the LeRobot EnvHub standard. env.py at the root exposes make_env() and is loadable via trust_remote_code=True.

python

from lerobot.envs.factory import make_env
envs = make_env("AshDash93/toy-sorting-env", trust_remote_code=True)

Quick Start

bash

# 1. Download scene assets from HuggingFace Hub
docker compose run sim uv run python assets/download.py --download

# 2. Install Isaac Lab + Isaac Sim (pip, Linux only)
docker compose run sim uv sync --extra sim

# 3. Visualise (headless)
docker compose run sim uv run python scripts/visualize_env.py --headless

# 3b. Visualise with GUI (X forwarding — run these on the server first)
#     ssh -X user@your-server-ip
#     xhost +local:docker
docker compose run sim uv run python scripts/visualize_env.py

Assets

On a developer machine with the original Lightwheel packs, extract assets locally:

bash

uv run python assets/download.py --extract   # populate assets/toy_sorting/
uv run python assets/download.py --upload    # push to HF Hub (needs HF_TOKEN in .env)

Layout

env.py                          # EnvHub entrypoint (make_env)
src/manipulator_learning/
  envhub.py                     # thin factory
  envs/
    toy_sorting_env.py          # ToySortingEnv (InteractiveScene-backed gym env)
    toy_sorting_scene_cfg.py    # InteractiveSceneCfg: table + robot + bowls + toys
    so_arm101_cfg.py            # ArticulationCfg for SO-ARM 101
    test_scene_cfg.py           # minimal scene for robot-only testing (no USD assets)
    zmq_server.py               # Phase 2 stub (sim↔train bridge)
  tasks/toy_sorting/
    task_spec.py                # high-level task spec (colors, bin names, instruction)
assets/
  download.py                   # asset extract / upload / download
scripts/
  visualize_env.py              # run full toy-sorting scene until window closed
  test_robot.py                 # minimal robot URDF test (no scene assets required)
  toy_sorting_envhub.py         # backwards-compat wrapper; prefer visualize_env.py
  inspect_assets.py             # inspect USD asset bounding boxes (usd-core only)

Attribution

Table asset (assets/toy_sorting/Table049/) is from the Lightwheel Kitchen pack by Lightwheel AI, licensed under CC BY-NC 4.0.

Toy shapes and sorting containers (assets/toy_sorting/Kit1/) are from the Lightwheel Toyroom pack by Lightwheel AI, licensed under CC BY-NC 4.0.

SO-ARM 101 URDF and meshes are from TheRobotStudio/SO-ARM100.