Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
2021/08/23 by Viktor Makoviychuk, Makoviychuk, Viktor, Lukasz Wawrzyniak +19 · 349 citations
Computer Science · #Advanced Neural Network Applications #Artificial intelligence #Artificial neural network #Classical mechanics #Computational science #Computer graphics (images) #Computer science #Distributed and Parallel Computing Systems #Dynamical simulation #FOS: Computer and information sciences #Host (biology) #Machine Learning (cs.LG) #Parallel Computing and Optimization Techniques #Physics #Reinforcement Learning in Robotics #Robot #Robotics #Robotics (cs.RO) #Simulation #Training (meteorology) #Variety (cybernetics) #cs.LG #cs.RO
paper · pdf · doi:10.48550/arxiv.2108.10470
published in arXiv (Cornell University) (Cornell University) · tech report on isaac-gym
openalex publication_date 2021/08/24 · arxiv created 2021/08/25 · arxiv updated 2021/08/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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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