Isaac Gym: High Performance GPU Based Physics Simulation For Robot Learning

Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, Gavriel State

Keywords: Simulation, Physics Engine, Reinforcement Learning, Robot Learning

TL;DR: We propose a new GPU based physics simulation for large scale high performance robot learning

Abstract: Isaac Gym offers a high-performance learning platform to train policies for a wide variety of robotics tasks entirely on GPU. Both physics simulation and neural network policy training reside on GPU and communicate by directly passing data from physics buffers to PyTorch tensors without ever going through CPU bottlenecks. This leads to blazing fast training times for complex robotics tasks on a single GPU with 2-3 orders of magnitude improvements compared to conventional RL training that uses a CPU-based simulator and GPUs for neural networks. We host the results and videos at https://sites.google.com/view/isaacgym-nvidia and Isaac Gym can be downloaded at https://developer.nvidia.com/isaac-gym. The benchmark and environments are available at https://github.com/NVIDIA-Omniverse/IsaacGymEnvs.

URL: https://github.com/NVIDIA-Omniverse/IsaacGymEnvs