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
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.
BibTeX
@inproceedings{
makoviychuk2021isaac,
title={Isaac Gym: High Performance {GPU} Based Physics Simulation For Robot Learning},
author={Viktor Makoviychuk and Lukasz Wawrzyniak and Yunrong Guo and Michelle Lu and Kier Storey and Miles Macklin and David Hoeller and Nikita Rudin and Arthur Allshire and Ankur Handa and Gavriel State},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=fgFBtYgJQX_}
}