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!

* The right video is played at 2x speed.

Diverse Objects

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!

Large-Scale Demonstration Dataset

We verify that all tasks are solvable, and have collected 4,000,000+ frames of demonstrations.

You can immediately study learning-from-demonstration algorithms without struggling with data collection!

Fast Visual RL Support

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!

* Tested on a workstation with Intel I9-9960X and NVIDIA Titan RTX using the PickCube environment.

3D Point Cloud Support

We provide 3D point cloud observations natively.

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

Realistic Depth Simulation

Our physics-grounded depth sensor simulator comes with realistic artifacts and is tested to have small sim2real domain gap.

Train your vision models in simulator and deploy in real world with limited additional efforts!

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

Interactive GUI

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

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


ManiSkill2 BEHAVIOR-1K Habitat 2.0 IssacGym MetaWorld ORBIT Robosuite
Fast rendering
Large-scale demonstrations
Object variations
Visual baselines
pip installable
3D point cloud support
Open-source engine
Hardware compatibility
Interactive GUI


Colab Tutorials

A step-by-step guide to help you get started with ManiSkill2. Try it now!

Reinforcement Learning
Imitation Learning
Advanced Rendering
Customize Environments


We organize challenges since 2021. Join the ongoing ManiSkill 2 Challenge!

Baselines & RL Library

We provide tuned baselines (BC, PPO, DAPG). Our RL Library, ManiSkill2-Learn, has been optimized for point cloud based RL.


We provide documentations for your reference.

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.

  • All
  • Articulation
  • Rigid Body
  • Soft Body
  • Stationary
  • Mobile
  • Single-Arm
  • Dual-Arm

Plug Charger

Rigid-Body, Stationary Manipulation

Move Bucket

Articulation, Mobile Manipulation


Soft-Body, Stationary Manipulation

Assembly Kit

Rigid-Body, Stationary Manipulation

Peg Insertion

Rigid-Body, Stationary Manipulation

Pick Clutter

Rigid-Body, Stationary Manipulation

Pick Cube

Rigid-Body, Stationary Manipulation


Rigid-Body, Stationary Manipulation

Pick YCB

Rigid-Body, Stationary Manipulation

Stack Cube

Rigid-Body, Stationary Manipulation

Turn Faucet

Articulation, Stationary Manipulation

Push Chair

Articulation, Mobile Manipulation

Open Cabinet Door

Articulation, Mobile Manipulation

Open Cabinet Drawer

Articulation, Mobile Manipulation


Soft-Body, Stationary Manipulation


Soft-Body, Stationary Manipulation


Soft-Body, Stationary Manipulation


Soft-Body, Stationary Manipulation


Soft-Body, Stationary Manipulation

Authors and BibTeX

1UC San Diego, 2Tsinghua University
and * indicate equal contribution (in alphabetic order).
  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},