BayesSimIG: IsaacGymを用いた適応的ドメインランダム化のためのスケーラブルなパラメータ推論
BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGym
シミュレーションパラメータの尤度不要推論に基づくドメインランダム化手法BayesSimをNVIDIA IsaacGymと統合し、GPUで大規模並列シミュレーションと推論を高速化するライブラリを提案した。
著者: Rika Antonova, Fabio Ramos, Rafael Possas, Dieter Fox
分類: cs.RO, cs.LG
原文アブストラクト
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.
