RoboTwin Bimanual Robotic Manipulation Platform

Lastest Version: RoboTwin 2.0 🤲 Webpage | Document | Paper | Community | Leaderboard

https://private-user-images.githubusercontent.com/88101805/463126988-e3ba1575-4411-4a36-ad65-f0b2f49890c3.mp4

RoboTwin 2.0 (ICML 2026) — Webpage · Doc · Paper · Talk · 机器之心 · Leaderboard

Earlier papers & challenge report

• 1.0 / Early — RoboTwin: Dual-Arm Robot Benchmark with Generative Digital Twins · CVPR 2025 (Highlight) PDF / arXiv · ECCV Workshop 2024 (Best Paper) PDF / arXiv
• CVPR'25 MEIS Challenge Report — PDF / arXiv · 量子位

📚 Overview

Prefer the RoboTwin Document for full guides — this README is a quick start.

RoboTwin 2.0 and RoboDojo share deployment via XPolicyLab: one policy-serving / eval stack across both benchmarks (single-task, multi-GPU, and remote server + local sim).

Default branch: main (RoboTwin 2.0).

Other branches (legacy / special-purpose)

• IsaacLab-Arena · RLinf_support · WBCD-2026
• RoboTwin-1.0 / early_version · gpt
• Challenge-Cup-2025 · CVPR-Challenge-2025-Round1 / Round2

🐣 Update

• 2026/08/03, We add XPolicyLab-based policy evaluation with single-task evaluation, multi-task multi-GPU scheduling, and remote policy-server/local-simulator deployment.
• 2026/03/03, We release RMBench, which is a memory-dependent manipulation benchmark built upon RoboTwin 2.0.
• 2026/02/20, Usage supported in StarVLA, which is a user-friendly codebase for VLA development.
• 2026/01/23, We update IsaacLab-Arena and RLinf support (contributed by RLinf team).
• 2025/08/28, We update the RoboTwin 2.0 Paper PDF.
• 2025/08/25, We fix ACT deployment code and update the leaderboard.
• 2025/08/06, We release RoboTwin 2.0 Leaderboard: leaderboard website.
• 2025/07/23, RoboTwin 2.0 received Outstanding Poster at ChinaSI 2025 (Ranking 1st).
• 2025/07/19, We Fix DP3 evaluation code error. We will update RoboTwin 2.0 paper next week.
• 2025/07/09, We update endpose control mode, please see [RoboTwin Doc - Usage - Control Robot] for more details.
• 2025/07/08, We upload Challenge-Cup-2025 Branch (第十九届挑战杯分支).
• 2025/07/02, Fix Piper Wrist Bug [issue]. Please redownload the embodiment asset.
• 2025/07/01, We release Technical Report of RoboTwin Dual-Arm Collaboration Challenge @ CVPR 2025 MEIS Workshop [arXiv] !
• 2025/06/21, We release RoboTwin 2.0 [Webpage] !
• 2025/04/11, RoboTwin is seclected as CVPR Highlight paper!
• 2025/02/27, RoboTwin is accepted to CVPR 2025 !
• 2024/09/30, RoboTwin (Early Version) received the Best Paper Award at the ECCV Workshop!
• 2024/09/20, Officially released RoboTwin.

🛠️ Installation

See RoboTwin 2.0 Document (Usage - Install & Download) for installation instructions. It takes about 20 minutes for installation.

XPolicyLab is embedded as a Git submodule. For a fresh checkout, clone RoboTwin recursively:

git clone --recurse-submodules https://github.com/RoboTwin-Platform/RoboTwin.git
cd RoboTwin

For an existing checkout, initialize the version pinned by RoboTwin:

git submodule update --init --recursive XPolicyLab

To pull the latest XPolicyLab commit on its configured main branch and refresh RoboTwin's submodule pin:

bash scripts/update_xpolicylab.sh
# optional: stage the pin / reinstall the editable package
bash scripts/update_xpolicylab.sh --stage --install

🤷‍♂️ Tasks Informations

See RoboTwin 2.0 Tasks Doc for more details.

🧑🏻‍💻 Usage

Document

Full usage details live in the RoboTwin Document — start from Usage. Prefer the Doc over this README when anything conflicts.

Getting Data

We provide over 100,000 pre-collected trajectories as part of the open-source release RoboTwin Dataset. We recommend downloading the pre-collected data (step 1) as the default path — it is ready to train on immediately. Collect data yourself (step 2) only when you need custom task configs, domain randomization, or embodiment setups.

1. Download XPolicyLab-Format Data (Recommended)

Download and extract all available XPolicyLab-format trajectories from Hugging Face:

bash scripts/download_xpolicylab_data.sh

To download only selected tasks, pass their names:

bash scripts/download_xpolicylab_data.sh adjust_bottle beat_block_hammer

Downloads and extractions both run in parallel (defaults: 8 workers each). As soon as a task ZIP finishes downloading, extraction starts without waiting for the rest. Tune concurrency with:

# Parallel download / extract workers (extract defaults to HF_MAX_WORKERS)
HF_MAX_WORKERS=8 HF_EXTRACT_WORKERS=16 bash scripts/download_xpolicylab_data.sh

Downloads land under data/demo_clean/<task_name>/aloha_agilex/data/ (note: self-collected data lands under data/<task_config>/... instead).

2. Task Running and Data Collection (Optional)

For custom task configs, domain randomization, or embodiment setups, collect data yourself. The following command will first search for a random seed for the target collection quantity, and then replay the seed to collect data.

bash collect_data.sh ${task_name} ${task_config} ${gpu_id}
# Example: bash collect_data.sh beat_block_hammer demo_randomized 0

Collected demonstrations are saved directly in the XPolicyLab trajectory format — no extra conversion step is needed:

data/<task_config>/<task_name>/<embodiment>/data/episode_0000000.hdf5

<embodiment> follows the embodiment field of the task config (aloha_agilex for the default aloha-agilex setup).

Migrating data collected with older RoboTwin versions

Data collected before the XPolicyLab-native format landed used the legacy raw layout data/<task_name>/<task_config>/data/episode0.hdf5. scripts/process_data_xpolicylab.py converts that legacy layout only; it is not needed for (and does not work on) freshly collected data.

3. Convert to LeRobot (Optional)

Many XPolicyLab policies train on LeRobot datasets. After you have XPolicyLab-format HDF5 under data/<task_config>/<task>/<embodiment>/data/ (from download or collection), convert with the shared scripts in XPolicyLab/scripts/.

Patterns are <task_config>.<task>.<embodiment> and may use * wildcards. They resolve against data/ next to the RoboTwin root (for example demo_clean.*.aloha_agilex). Keep --data_type as the default RoboDojo — RoboTwin XPolicyLab trajectories share that HDF5 layout.

Run in an environment that already has the matching LeRobot package (v2.1 vs v3.0). Conversion writes under HF_LEROBOT_HOME (default ~/.cache/huggingface/lerobot); point it at a large disk if needed:

export HF_LEROBOT_HOME=/path/with/enough/space/lerobot

# LeRobot v2.1 — all demo_clean tasks
python XPolicyLab/scripts/transform_lerobot_v21_format.py \
  "demo_clean.*.aloha_agilex" \
  --repo_id robotwin_demo_clean_aloha_agilex \
  --max_episode 50

# LeRobot v3.0 — same selection
python XPolicyLab/scripts/transform_lerobot_v30_format.py \
  "demo_clean.*.aloha_agilex" \
  --repo_id robotwin_demo_clean_aloha_agilex_v30 \
  --max_episode 50

# Single task
python XPolicyLab/scripts/transform_lerobot_v21_format.py \
  "demo_clean.beat_block_hammer.aloha_agilex" \
  --repo_id beat_block_hammer_demo_clean

Useful flags: --repo_id (output dataset name), --max_episode (cap episodes per task/embodiment), --resolution HxW or --image_height / --image_width (default: auto-detect; RoboTwin is often 240x320). Output lands at ${HF_LEROBOT_HOME}/<repo_id>.

4. Modify Task Config

☝️ See RoboTwin 2.0 Tasks Configurations Doc for more details.

Task settings such as demo_clean and demo_randomized are stored in env_cfg/task_config/.

5. Evaluate Policies via XPolicyLab

All evaluation goes through scripts/eval_policy.sh. The policy adapter must exist under XPolicyLab/policy/<policy_name>/ (see the XPolicyLab policy catalog).

--env-cfg-type selects the XPolicyLab action profile (validated against XPolicyLab/utils/robot/_robot_info.json; arx_x5 matches RoboTwin's default aloha-agilex layout), while the simulator embodiment stays controlled by --task-config.

Local evaluation (multi-task, multi-GPU). The scheduler starts a policy server and simulator per task on your GPU pool. Task lists and GPU settings live in env_cfg/eval/all_tasks.yml (trim tasks to a single entry for single-task evaluation):

bash scripts/eval_policy.sh multitask \
  --config env_cfg/eval/all_tasks.yml \
  --policy-name <policy_name> \
  --ckpt-name <checkpoint> \
  --env-cfg-type arx_x5 \
  --policy-conda-env <policy_env> \
  --eval-env-conda-env <robotwin_env> \
  --action-type <action_type>

Add --dry-run to validate the schedule without launching anything. Results are written to eval_result/multitask/ by default.

Split deployment (remote policy server + local simulator). Start the server pool on the policy host, then point the local scheduler at it:

# On the policy-server host (fill in the placeholders first):
bash scripts/eval_policy.sh serve --config env_cfg/eval/remote_server.yml

# On the simulator host:
bash scripts/eval_policy.sh multitask \
  --config env_cfg/eval/all_tasks.yml \
  --policy-name <policy_name> \
  --env-cfg-type arx_x5 \
  --eval-env-conda-env <robotwin_env> \
  --enable-remote \
  --policy-server-ip <server_ip> --policy-server-port <port>

--policy-server-ip/--policy-server-port can be repeated to use a server pool, or configured once via enable_remote / policy_server_ip / policy_server_port in the eval config.

🏄‍♂️ Experiment & LeaderBoard

We recommend that the RoboTwin Platform can be used to explore the following topics: 1. single - task fine - tuning capability 2. visual robustness 3. language diversity robustness (language condition) 4. multi-tasks capability 5. cross-embodiment performance

The full leaderboard and setting can be found in: https://robotwin-platform.github.io/leaderboard.

💽 Pre-collected Large-scale Dataset

Please refer to RoboTwin 2.0 Dataset - Huggingface.

👍 Citations

If you find our work useful, please consider citing:

RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation ``` @article{chen2025robotwin, title={Robotwin 2.0: A scalable data generator and benchmark with strong domain randomization for robust bimanual robotic manipulation}, author={Chen, Tianxing and Chen, Zanxin and Chen, Baijun and Cai, Zijian and Liu, Yibin and Li, Zixuan and Liang, Qiwei and Lin, Xianliang and Ge, Yiheng and Gu, Zhe