Isaac Gym：ロボット学習のためのGPUベース高性能物理シミュレーション
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
物理シミュレーションとニューラルネットワーク学習を両方GPU上で実行し、CPUボトルネックを排除することで、ロボット強化学習の訓練を従来比2〜3桁高速化したプラットフォームを提案。
著者: Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, Gavriel State
分類: cs.RO, cs.LG
原文アブストラクト
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}.
