206 citations · 340 across the 26 of their papers we have counts for
3 papers · 1 filter
A Bayesian Treatment of Real-to-Sim for Deformable Object Manipulation
Rika Antonova, Jingyun Yang, Priya Sundaresan +3
Deformable object manipulation remains a challenging task in robotics research. Conventional techniques for parameter inference and state estimation typically rely on a precise def…
Learning Periodic Tasks from Human Demonstrations
Jingyun Yang, Junwu Zhang, Connor Settle +3
We develop a method for learning periodic tasks from visual demonstrations. The core idea is to leverage periodicity in the policy structure to model periodic aspects of the tasks.…
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:…