Easy To Get Started

ManiSkill2 is pip installable and directly runnable in Google Colab.

Starting from a clean Python environment, you can see a robot working in 1 minute.

Just copy & paste the commands below!

• 01 Install ManiSkill2 by pip

  pip install mani-skill2

• 02 Download Assets and Demos

  python -m mani_skill2.utils.download_asset ycb
python -m mani_skill2.utils.download_asset minimal_bedroom
python -m mani_skill2.utils.download_demo PickSingleYCB-v0

• 03 Replay a Demonstration Trajectory

  python -m mani_skill2.trajectory.replay_trajectory --traj-path demos/v0/rigid_body/PickSingleYCB-v0/035_power_drill.h5 --vis --bg-name minimal_bedroom

* The right video is played at 2x speed.

ManiSkill2 features out-of-the-box tasks for 20 skills with 2,000+ objects.

You don't have to collect assets or design tasks by yourself, and can focus on algorithms!

ManiSkill2 provides blazingly fast visual RL environments using our asynchronized rendering system.

On a regular workstation, your robot can interact with the environment and collect millions of visual observations within just 10 min!

We provide 3D point cloud observations natively.

Try your favorite 3D learning algorithms and develop your own!

Realistic Depth Simulation

* See our recent T-RO paper "Close the Optical Sensing Domain Gap by Physics-Grounded Active Stereo Sensor Simulation".

We provide an interactive GUI to inspect and interact with the environments.

See what's going on and what's going wrong!

Diverse Task Families Out-of-the-Box

ManiSkill2 embraces a heterogeneous collection of out-of-the-box task families for 20 manipulation skills.
Distinct types of manipulation tasks are covered: stationary/mobile-base, single/dual-arm, rigid/soft-body.

* All scenes below are rendered in SAPIEN with ray tracing. However, the scenes cannot be released due to licenses.

¹UC San Diego, ²Tsinghua University

^† and ^* indicate equal contribution (in alphabetic order).

@inproceedings{gu2023maniskill2,
  title={ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills},
  author={Gu, Jiayuan and Xiang, Fanbo and Li, Xuanlin and Ling, Zhan and Liu, Xiqiang and Mu, Tongzhou and Tang, Yihe and Tao, Stone and Wei, Xinyue and Yao, Yunchao and Yuan, Xiaodi and Xie, Pengwei and Huang, Zhiao and Chen, Rui and Su, Hao},
  booktitle={International Conference on Learning Representations},
  year={2023}
}