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arXiv:2108.10470cs.RO

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, Arthur Allshire, Ankur Handa, Gavriel State

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}.

arXiv comments
tech report on isaac-gym
Journal reference
DOI
Subjects
cs.RORobotics cs.LGMachine Learning

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Discussed in comments2 threads

    • My understanding is that it's in vogue to use deep learning for complex control problems, and the results are fairly impressive. The idea is to train robotic motion end to end with RL. Not an expert so I don't know the strength and weaknesses versus classical approaches.…

      mmmoreView on HNParent context

    • blogpost author here. This is not exactly the claim. I’m kind of certain they do “fair tests” where they test the same scenarios in different simulators. The problem however is that these tests are not indicative of anything useful. Why should one care if you get 430,000x real…

      stonet2000View on HNParent context

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