Demonstrating RFUniverse: A Multiphysics Simulation Platform for Embodied AI

Haoyuan Fu
Shanghai Jiao Tong University
Wenqiang Xu
Shanghai Jiao Tong University
Ruolin Ye
Cornell University
Han Xue
Shanghai Jiao Tong University
Zhenjun Yu
Shanghai Jiao Tong University
Tutian Tang
Shanghai Jiao Tong University
Yutong Li
Shanghai Jiao Tong University
Wenxin Du
Shanghai Jiao Tong University
Jieyi Zhang
Shanghai Jiao Tong University
Cewu Lu
Shanghai Jiao Tong University
Paper Website

Paper ID 87

Session 11. Control & Dynamics


Poster Session Thursday, July 13

Poster 23

Abstract: Multiphysics phenomena, the coupling effects involving different aspects of physics laws, are pervasive in the real world and can often be encountered when performing everyday household tasks. Intelligent agents which seek to assist or replace human laborers will need to learn to cope with such phenomena in household task settings. To equip the agents with such kind of abilities, the research community needs a simulation environment, which will have the capability to serve as the testbed for the training process of these intelligent agents, to have the ability to support multiphysics coupling effects.

Though many mature simulation software for multiphysics simulation have been adopted in industrial production, such techniques have not been applied to robot learning or embodied AI research. To bridge the gap, we propose a novel simulation environment named RFUniverse. This simulator can not only compute rigid and multi-body dynamics, but also multiphysics coupling effects commonly observed in daily life, such as air-solid interaction, fluid-solid interaction, and heat transfer.

Because of the unique multiphysics capacities of this simulator, we can benchmark tasks that involve complex dynamics due to multiphysics coupling effects in a simulation environment before deploying to the real world. RFUniverse provides multiple interfaces to let the users interact with the virtual world in various ways, which is helpful and essential for learning, planning, and control. We benchmark three tasks with reinforcement learning, including food cutting, water pushing, and towel catching. We also evaluate butter pushing with a classic planning-control paradigm. This simulator offers an enhancement of physics simulation in terms of the computation of multiphysics coupling effects. The simulation environment, videos, and other supplementary materials can be viewed on the website: https: //