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Update README.md #280

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8 changes: 6 additions & 2 deletions README.md
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# ManiSkill2
# ManiSkill

![teaser](figures/teaser_v2.jpg)

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[![Discord](https://img.shields.io/discord/996566046414753822?logo=discord)](https://discord.gg/x8yUZe5AdN)
<!-- [![Docs](https://github.com/haosulab/ManiSkill2/actions/workflows/gh-pages.yml/badge.svg)](https://haosulab.github.io/ManiSkill2) -->

ManiSkill2 is a unified benchmark for learning generalizable robotic manipulation skills powered by [SAPIEN](https://sapien.ucsd.edu/). **It features 20 out-of-box task families with 2000+ diverse object models and 4M+ demonstration frames**. Moreover, it empowers fast visual input learning algorithms so that **a CNN-based policy can collect samples at about 2000 FPS with 1 GPU and 16 processes on a workstation**. The benchmark can be used to study a wide range of algorithms: 2D & 3D vision-based reinforcement learning, imitation learning, sense-plan-act, etc.
ManiSkill is a unified benchmark for learning generalizable robotic manipulation skills powered by [SAPIEN](https://sapien.ucsd.edu/). **It features 20 out-of-box task families with 2000+ diverse object models and 4M+ demonstration frames**. Moreover, it empowers fast visual input learning algorithms so that **a CNN-based policy can collect samples at about 2000 FPS with 1 GPU and 16 processes on a workstation**. The benchmark can be used to study a wide range of algorithms: 2D & 3D vision-based reinforcement learning, imitation learning, sense-plan-act, etc.

Currently the main branch here is ManiSkill v2, we are merging in the GPU parallelized state/visual simulation to the main branch in the next few weeks to start an open beta release. Stay tuned!

Note previously there was previously a ManiSkill and ManiSkill2, we are rebranding it all to just ManiSkill and the python package versioning tells you which iteration (3.0.0 now means ManiSkill3)

Please refer to our [documentation](https://haosulab.github.io/ManiSkill2) to learn more information. There are also hands-on [tutorials](examples/tutorials) (e.g, [quickstart colab tutorial](https://colab.research.google.com/github/haosulab/ManiSkill2/blob/main/examples/tutorials/1_quickstart.ipynb)).

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