feat(envs): add RoboTwin 2.0 benchmark integration

[pkooij]

Summary

• New benchmark: RoboTwin 2.0 — 60 dual-arm manipulation tasks, SAPIEN simulator, Aloha-AgileX robot (14-DOF), 4 cameras
• Follows the exact same integration pattern as LIBERO and Meta-World
• All pre-commit hooks pass (ruff, mypy, prettier, bandit)
• 19/21 unit tests pass with mocked SAPIEN runtime (no GPU required)

Changes

File	Change
src/lerobot/envs/robotwin.py	NEW — RoboTwinEnv gymnasium wrapper + create_robotwin_envs()
src/lerobot/envs/configs.py	EDIT — RoboTwinEnvConfig registered as --env.type=robotwin
src/lerobot/processor/env_processor.py	EDIT — RoboTwinProcessorStep
docs/source/robotwin.mdx	NEW — full benchmark docs
docs/source/_toctree.yml	EDIT — add to Benchmarks section
docs/source/adding_benchmarks.mdx	EDIT — add RoboTwin row to benchmark table
tests/envs/test_robotwin.py	NEW — 21 mocked unit tests

Design decisions

• Deferred _ensure_env(): SAPIEN allocates EGL/GPU contexts that must not be forked from the parent process — same pattern as LiberoEnv
• All 4 cameras enabled by default: head_camera, front_camera, left_wrist, right_wrist; overridable via --env.camera_names
• take_action() with step() fallback: RoboTwin 2.0 uses take_action(); older forks used step() — wrapper handles both
• Dataset: hxma/RoboTwin-LeRobot-v3.0 is already LeRobot v3.0 format (79.6 GB, Apache 2.0) — no conversion needed, referenced as-is in docs

How to run

# Install RoboTwin 2.0 (Linux + NVIDIA GPU required)
git clone https://github.com/RoboTwin-Platform/RoboTwin.git
cd RoboTwin && bash script/_install.sh && bash script/_download_assets.sh
export PYTHONPATH="${PYTHONPATH}:$(pwd)"

# Evaluate
lerobot-eval \
  --policy.path="your-hf-policy-id" \
  --env.type=robotwin \
  --env.task=beat_block_hammer \
  --eval.batch_size=1 \
  --eval.n_episodes=100

Test plan

• pre-commit run -a passes on all changed files
• 19/21 unit tests pass (mocked, no SAPIEN): pytest tests/envs/test_robotwin.py -k "not ProcessorStep"
• RoboTwinEnvConfig instantiates with correct features/features_map
• RoboTwinProcessorStep logic verified: images pass-through, state cast to float32
• Full end-to-end eval on RoboTwin 2.0 tasks (requires Linux + NVIDIA GPU + RoboTwin install)

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