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

TL;DR

Isaac Gym is a GPU-based physics simulation platform that enables rapid training of robotic policies by integrating physics simulation and neural network training on GPU, significantly accelerating reinforcement learning processes.

Contribution

The paper introduces Isaac Gym, a GPU-native physics simulation environment that drastically reduces training time for robotics policies compared to CPU-based simulators.

Findings

Achieves 2-3 orders of magnitude faster training times.

Supports training for a wide variety of robotics tasks.

Enables direct GPU-to-GPU data transfer for efficient computation.

Abstract

Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU. Both physics simulation and the neural network policy training reside on GPU and communicate by directly passing data from physics buffers to PyTorch tensors without ever going through any 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 GPU for neural networks. We host the results and videos at \url{https://sites.google.com/view/isaacgym-nvidia} and isaac gym can be downloaded at \url{https://developer.nvidia.com/isaac-gym}.