ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI

TL;DR

ManiSkill3 is a GPU-accelerated robotics simulation platform that significantly speeds up training and supports diverse, contact-rich manipulation tasks for generalizable embodied AI research.

Contribution

It introduces ManiSkill3, the fastest GPU-parallelized robotics simulator supporting a wide range of environments and tasks, with extensive benchmarks and baseline algorithms.

Findings

Simulation speed up to 30,000+ FPS

10-1000x faster than existing platforms

Supports 12 diverse manipulation domains

Abstract

Simulation has enabled unprecedented compute-scalable approaches to robot learning. However, many existing simulation frameworks typically support a narrow range of scenes/tasks and lack features critical for scaling generalizable robotics and sim2real. We introduce and open source ManiSkill3, the fastest state-visual GPU parallelized robotics simulator with contact-rich physics targeting generalizable manipulation. ManiSkill3 supports GPU parallelization of many aspects including simulation+rendering, heterogeneous simulation, pointclouds/voxels visual input, and more. Simulation with rendering on ManiSkill3 can run 10-1000x faster with 2-3x less GPU memory usage than other platforms, achieving up to 30,000+ FPS in benchmarked environments due to minimal python/pytorch overhead in the system, simulation on the GPU, and the use of the SAPIEN parallel rendering system. Tasks that used to take hours to train can now take minutes. We further provide the most comprehensive range of GPU parallelized environments/tasks spanning 12 distinct domains including but not limited to mobile manipulation for tasks such as drawing, humanoids, and dextrous manipulation in realistic scenes designed by artists or real-world digital twins. In addition, millions of demonstration frames are provided from motion planning, RL, and teleoperation. ManiSkill3 also provides a comprehensive set of baselines that span popular RL and learning-from-demonstrations algorithms.