BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGym

• URL: http://arxiv.org/abs/2107.04527v1
• Date: Fri, 9 Jul 2021 16:21:31 GMT
• Title: BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGym
• Authors: Rika Antonova, Fabio Ramos, Rafael Possas, Dieter Fox
• Abstract summary: BayesSimIG is a library that provides an implementation of BayesSim integrated with the recently released NVIDIA IsaacGym. BayesSimIG provides an integration with NVIDIABoard to easily visualize slices of high-dimensional posteriors.
• Score: 59.53949960353792
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: BayesSim is a statistical technique for domain randomization in reinforcement learning based on likelihood-free inference of simulation parameters. This paper outlines BayesSimIG: a library that provides an implementation of BayesSim integrated with the recently released NVIDIA IsaacGym. This combination allows large-scale parameter inference with end-to-end GPU acceleration. Both inference and simulation get GPU speedup, with support for running more than 10K parallel simulation environments for complex robotics tasks that can have more than 100 simulation parameters to estimate. BayesSimIG provides an integration with TensorBoard to easily visualize slices of high-dimensional posteriors. The library is built in a modular way to support research experiments with novel ways to collect and process the trajectories from the parallel IsaacGym environments.

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