From 64ee81770d4d84138f5e5154fffdf148b6cecbee Mon Sep 17 00:00:00 2001 From: Stone Tao Date: Sat, 20 Apr 2024 16:35:41 -0700 Subject: [PATCH] Update README.md --- README.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 80e35b3ea..3e4c888c8 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# ManiSkill2 +# ManiSkill ![teaser](figures/teaser_v2.jpg) @@ -8,7 +8,11 @@ [![Discord](https://img.shields.io/discord/996566046414753822?logo=discord)](https://discord.gg/x8yUZe5AdN) -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)).