Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing

Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing

© 2018 IEEE. An efficient, generalizable physical simulator with universal uncertainty estimates has wide applications in robot state estimation, planning, and control. In this paper, we build such a simulator for two scenarios, planar pushing and ball bouncing, by augmenting an analytical rigid-bod...

Full description

Bibliographic Details
Main Authors: Ajay, Anurag, Wu, Jiajun, Fazeli, Nima, Bauza, Maria, Kaelbling, Leslie P., Tenenbaum, Joshua B., Rodriguez, Alberto
Other Authors: Massachusetts Institute of Technology. Department of Mechanical Engineering
Format: Article
Language:English
Published: IEEE 2021
Online Access:https://hdl.handle.net/1721.1/137711

Internet

https://hdl.handle.net/1721.1/137711

Similar Items