RoboTwin 2.0#

本节介绍 LeRobot 的通用功能。请按目标机器人、数据集和运行环境配置示例；用于 AlohaMini 2 / 2 Pro 前，需要确认硬件与接口兼容性。

RoboTwin 2.0 is a large-scale dual-arm manipulation benchmark built on the SAPIEN physics engine. It provides a standardized evaluation protocol for bimanual robotic policies across 50 tasks (as of upstream main) with strong domain randomization (clutter, lighting, background, tabletop height, and language instructions).

Paper: RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
GitHub: RoboTwin-Platform/RoboTwin
Leaderboard: robotwin-platform.github.io/leaderboard
Dataset: lerobot/robotwin_unified

RoboTwin 2.0 benchmark overview

Overview#

Property	Value
Tasks	50 dual-arm manipulation tasks
Robot	Aloha-AgileX bimanual (14 DOF, 7 per arm)
Action space	14-dim joint-space, continuous in `[-1, 1]`
Cameras	`head_camera`, `left_camera`, `right_camera`
Simulator	SAPIEN (not MuJoCo)
Eval protocol	100 episodes/task, 50 demo_clean demonstrations
Eval settings	Easy (`demo_clean`) and Hard (`demo_randomized`)

Available tasks#

RoboTwin 2.0 ships 50 dual-arm manipulation tasks in its upstream envs/ directory. The canonical list is the ROBOTWIN_TASKS tuple in src/lerobot/envs/robotwin.py, mirrored verbatim from the upstream repo. Example tasks:

Task	CLI name	Category
Beat block with hammer	`beat_block_hammer`	Tool use
Click bell / alarm clock	`click_bell`	Precision press
Stack blocks (2 / 3)	`stack_blocks_two/three`	Stacking
Stack bowls (2 / 3)	`stack_bowls_two/three`	Stacking
Handover block / mic	`handover_block`	Bimanual coord.
Lift pot	`lift_pot`	Bimanual lift
Shake bottle	`shake_bottle`	Continuous motion
Turn switch	`turn_switch`	Articulated obj
Stamp seal	`stamp_seal`	Precision place
Scan object	`scan_object`	Mobile manip.

Pass a comma-separated list to --env.task to run multiple tasks in a single eval sweep.

提示

open_laptop is currently broken upstream (its check_success() uses self.arm_tag, which is only set inside the scripted-expert play_once() path and therefore unavailable during normal policy eval). Avoid it until the upstream bug is fixed, or patch the task to default self.arm_tag = "left" in load_actors().

Dataset#

The RoboTwin 2.0 dataset is available in LeRobot v3.0 format on the Hugging Face Hub:

lerobot/robotwin_unified

It contains over 100,000 pre-collected trajectories across all 50 tasks (79.6 GB, Apache 2.0 license). No format conversion is needed — it is already in the correct LeRobot v3.0 schema with video observations and action labels.

You can load it directly with the HF Datasets library:

from datasets import load_dataset

ds = load_dataset("lerobot/robotwin_unified", split="train")

Installation#

RoboTwin 2.0 requires Linux with an NVIDIA GPU (CUDA 12.1 recommended). Installation takes approximately 20 minutes.

1. Create a conda environment#

conda create -n robotwin python=3.10 -y
conda activate robotwin

2. Install LeRobot#

git clone https://github.com/huggingface/lerobot.git
cd lerobot
pip install -e "."

3. Install RoboTwin 2.0#

git clone https://github.com/RoboTwin-Platform/RoboTwin.git
cd RoboTwin
bash script/_install.sh
bash script/_download_assets.sh

The install script handles all Python dependencies including SAPIEN, CuRobo, mplib, and pytorch3d.

提示

If the automated install fails, install manually:

pip install -r requirements.txt
pip install "git+https://github.com/facebookresearch/pytorch3d.git@stable"
cd envs && git clone https://github.com/NVlabs/curobo.git && cd curobo
pip install -e . --no-build-isolation

Then apply the required mplib fix: in mplib/planner.py line 807, remove or collide from the conditional.

4. Add RoboTwin to PYTHONPATH#

The RoboTwin task modules must be importable by LeRobot. From within the RoboTwin/ directory:

export PYTHONPATH="${PYTHONPATH}:$(pwd)"

Add this to your shell profile to make it permanent.

Evaluation#

Standard evaluation (recommended)#

Evaluate a policy on a single task with the official protocol (100 episodes):

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

Single-task quick check#

lerobot-eval \
  --policy.path="your-hf-policy-id" \
  --env.type=robotwin \
  --env.task=beat_block_hammer \
  --eval.batch_size=1 \
  --eval.n_episodes=5

Multi-task sweep#

Evaluate on several tasks in one run:

lerobot-eval \
  --policy.path="your-hf-policy-id" \
  --env.type=robotwin \
  --env.task=beat_block_hammer,click_bell,handover_block,stack_blocks_two \
  --eval.batch_size=1 \
  --eval.n_episodes=100

Full benchmark (all 50 tasks)#

lerobot-eval \
  --policy.path="your-hf-policy-id" \
  --env.type=robotwin \
  --env.task=adjust_bottle,beat_block_hammer,blocks_ranking_rgb,blocks_ranking_size,click_alarmclock,click_bell,dump_bin_bigbin,grab_roller,handover_block,handover_mic,hanging_mug,lift_pot,move_can_pot,move_pillbottle_pad,move_playingcard_away,move_stapler_pad,open_microwave,pick_diverse_bottles,pick_dual_bottles,place_a2b_left,place_a2b_right,place_bread_basket,place_bread_skillet,place_burger_fries,place_can_basket,place_cans_plasticbox,place_container_plate,place_dual_shoes,place_empty_cup,place_fan,place_mouse_pad,place_object_basket,place_object_scale,place_object_stand,place_phone_stand,place_shoe,press_stapler,put_bottles_dustbin,put_object_cabinet,rotate_qrcode,scan_object,shake_bottle,shake_bottle_horizontally,stack_blocks_three,stack_blocks_two,stack_bowls_three,stack_bowls_two,stamp_seal,turn_switch \
  --eval.batch_size=1 \
  --eval.n_episodes=100

提示

open_laptop is intentionally omitted above because of the upstream self.arm_tag bug (see the Available tasks section). Re-add it once the upstream fix lands.

Camera configuration#

By default, all three cameras are included:

Camera key	Description
`head_camera`	Torso-mounted overhead view
`left_camera`	Left arm wrist-mounted camera
`right_camera`	Right arm wrist-mounted camera

To use a subset of cameras, override --env.camera_names:

lerobot-eval \
  --policy.path="your-hf-policy-id" \
  --env.type=robotwin \
  --env.task=beat_block_hammer \
  --env.camera_names="head_camera,left_camera" \
  --eval.batch_size=1 \
  --eval.n_episodes=10

Environment config reference#

Key parameters for RoboTwinEnvConfig:

Parameter	Default	Description
`task`	`"beat_block_hammer"`	Comma-separated task name(s)
`fps`	`25`	Simulation FPS
`episode_length`	`300`	Max steps per episode
`obs_type`	`"pixels_agent_pos"`	`"pixels"` or `"pixels_agent_pos"`
`camera_names`	`"head_camera,left_camera,right_camera"`	Comma-separated active cameras
`observation_height`	`240`	Camera pixel height
`observation_width`	`320`	Camera pixel width

Leaderboard submission#

Results can be submitted to the RoboTwin 2.0 leaderboard. The official protocol requires:

Training on 50 demo_clean demonstrations per task
Evaluating 100 episodes per task
Reporting success rate separately for Easy (demo_clean) and Hard (demo_randomized) settings

For submission instructions, refer to the RoboTwin 2.0 documentation.