1 citations · 2 across the 2 of their papers we have counts for
3 papers
BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGym
Rika Antonova, Fabio Ramos, Rafael Possas +1
BayesSim is a statistical technique for domain randomization in reinforcement learning based on likelihood-free inference of simulation parameters. This paper outlines BayesSimIG:…
DISCO: Double Likelihood-free Inference Stochastic Control
Lucas Barcelos, Rafael Oliveira, Rafael Possas +2
Accurate simulation of complex physical systems enables the development, testing, and certification of control strategies before they are deployed into the real systems. As simulat…
BayesSim: adaptive domain randomization via probabilistic inference for robotics simulators
Fabio Ramos, Rafael Carvalhaes Possas, Dieter Fox
We introduce BayesSim, a framework for robotics simulations allowing a full Bayesian treatment for the parameters of the simulator. As simulators become more sophisticated and able…