# BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGym

arXiv: https://arxiv.org/abs/2107.04527 · HTML: https://mlsanity.com/paper/2107.04527/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 9 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience](https://mlsanity.com/paper/1810.05687/) (2018)
- [BayesSim: adaptive domain randomization via probabilistic inference for robotics simulators](https://mlsanity.com/paper/1906.01728/) (2019)
- [Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU](https://mlsanity.com/paper/2001.00706/) (2020)
- [Inferring the Material Properties of Granular Media for Robotic Tasks](https://mlsanity.com/paper/2003.08032/) (2020)
- [STReSSD: Sim-To-Real from Sound for Stochastic Dynamics](https://mlsanity.com/paper/2011.03136/) (2020)
- [A User's Guide to Calibrating Robotics Simulators](https://mlsanity.com/paper/2011.08985/) (2020)
- [DISCO: Double Likelihood-free Inference Stochastic Control](https://mlsanity.com/paper/2002.07379/) (2020)

## Built on by (most cited first, 3 of 3)

- [AdaptSim: Task-Driven Simulation Adaptation for Sim-to-Real Transfer](https://mlsanity.com/paper/2302.04903/) (2023)
- [A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards](https://mlsanity.com/paper/2502.08643/) (2025)
- [Estimating Material Properties of Interacting Objects Using Sum-GP-UCB](https://mlsanity.com/paper/2310.11749/) (2023)

## Abstract (arXiv)

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.