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

2021/08/23 by Viktor Makoviychuk, Makoviychuk, Viktor, Lukasz Wawrzyniak +19 · 193 citations
Computer Science · #Advanced Neural Network Applications #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #Machine Learning (cs.LG) #Parallel Computing and Optimization Techniques #Reinforcement Learning in Robotics #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.2108.10470

openalex publication_date 2021/08/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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 \urlhttps://sites.google.com/view/isaacgym-nvidia and isaac gym can be downloaded at \urlhttps://developer.nvidia.com/isaac-gym.

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