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

Abstract

Simulation has enabled unprecedented compute-scalable approaches to robotics. However, many existing simulators typically support a narrow range of tasks and lack features critical for scaling generalizable robotics and sim2real. We introduce ManiSkill3, a state-of-the-art 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, and more. GPU simulation+rendering uses 2-4x less GPU memory compared to other platforms and achieves up to 30,000+ FPS in benchmarked environments due to minimal overhead, simulation on the GPU, and the use of the SAPIEN parallel rendering system, enabling visual RL to solve tasks in minutes instead of hours. We further provide the most comprehensive range of tasks spanning 12 distinct domains including but not limited to mobile manipulation, 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.