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Update README.md
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README.md
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[](https://discord.gg/x8yUZe5AdN)
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<!-- [](https://haosulab.github.io/ManiSkill2) -->
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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.
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This is the huggingface datasets page for all data related to [ManiSkill2](https://github.com/haosulab/ManiSkill2),
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including **assets, robot demonstrations, and pretrained models**
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[](https://discord.gg/x8yUZe5AdN)
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<!-- [](https://haosulab.github.io/ManiSkill2) -->
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**Update: ManiSkill 3 has been released https://github.com/haosulab/ManiSkill/. It uses different datasets than ManiSkill2 so the data here is not expected to transfer over**
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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.
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This is the huggingface datasets page for all data related to [ManiSkill2](https://github.com/haosulab/ManiSkill2),
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including **assets, robot demonstrations, and pretrained models**
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