Skip to content

RoboTwin 2.0

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).

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.

Warning

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.

Warning

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

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

Tip

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.