BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGym

Understand

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.