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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Manipulation Skill Framework, an open source GPU parallelized robotics simulator and benchmark
| Date | Stars |
|---|---|
| 2026-07-24 | 3152 |
| 2026-07-25 | 3156 |
| 2026-07-28 | 3156 |
| 2026-07-30 | 3156 |
| 2026-08-06 | 3156 |
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# ManiSkill 3  <p style="text-align: center; font-size: 0.8rem; color: #999;margin-top: -1rem;">Sample of environments/robots rendered with ray-tracing. Scene datasets sourced from AI2THOR and ReplicaCAD</p> [](https://pepy.tech/project/mani_skill) [](https://colab.research.google.com/github/mani-skill/ManiSkill/blob/main/examples/tutorials/1_quickstart.ipynb) [](https://badge.fury.io/py/mani-skill) [](https://maniskill.readthedocs.io/en/latest/) [](https://discord.gg/x8yUZe5AdN) ManiSkill is an open-source framework for robot simulation and training powered by [SAPIEN](https://sapien.ucsd.edu/), with a strong focus on manipulation skills. Among its features include: - GPU parallelized visual data collection system. On the high end you can collect RGBD + Segmentation data at 30,000+ FPS on a 4090 GPU - GPU parallelized simulation, enabling high throughput state-based synthetic data collection in simulation - GPU parallelized heterogeneous simulation, where every parallel environment has a completely different scene/set of objects - Example tasks cover a wide range of different robot embodiments (humanoids, mobile manipulators, single-arm robots) as well as a wide range of different tasks (table-top, drawing/cleaning, dexterous manipulation) - Flexible and simple task building API that abstracts away much of the complex GPU memory management code via an object oriented design - Real2sim environments for scalably evaluating real-world policies 100x faster via GPU simulation. - Sim2real examples for deploying policies trained in simulation to the real world - Many tuned robot learning baselines in Reinforcement Learning (e.g. PPO, SAC, [TD-MPC2](https://github.com/nicklashansen/tdmpc2)), Imitation Learning (e.g. Behavior Cloning, [Diffusion Policy](https://github.com/real-stanford/diffusion_policy)), and large Vision Language Action (VLA) models (e.g. [Octo](https://github.com/octo-models/octo), [RDT-1B](https://github.com/thu-ml/RoboticsDiffusionTransformer), [RT-x](https://robotics-transformer-x.github.io/)) For more details we encourage you to take a look at our [paper](https://arxiv.org/abs/2410.00425), published at [RSS 2025](https://roboticsconference.org/). Please refer to our [documentation](https://maniskill.readthedocs.io/en/latest/user_guide) to learn more information from tutorials on building tasks to sim2real to running baselines. If you find any bugs or have any feature requests please post them to our [GitHub issues](https://github.com/mani-skill/ManiSkill/issues/) or discuss about them on [GitHub discussions](https://github.com/mani-skill/ManiSkill/discussions/). We also have a [Discord Server](https://discord.gg/x8yUZe5AdN) through which we make announcements and discuss about ManiSkill. Users looking for the original ManiSkill2 can find the commit for that codebase at the [v0.5.3 tag](https://github.com/mani-skill/ManiSkill/tree/v0.5.3) ## Installation Installation of ManiSkill is extremely simple, you only need to run a few pip installs and setup Vulkan for rendering. ```bash # install the package pip install --upgrade mani_skill # install a version of torch that is compatible with your system pip install torch ``` Finally you also need to set up Vulkan with [instructions here](https://maniskill.readthedocs.io/en/latest/user_guide/getting_started/installation.html#vulkan) For more details about installation (e.g. from source, or doing troubleshooting) see [the documentation](https://maniskill.readthedocs.io/en/latest/user_guide/getting_started/installation.html ) ## Getting Started To get started, check out the quick start documentation: https
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7de0936760622ffe, topic:robotics, topic:embodied-ai, topic:simulation