Robotics: Science and Systems XIX

GranularGym: High Performance Simulation for Robotic Tasks with Granular Materials

David R Millard, Daniel Pastor, Joseph Bowkett, Paul Backes, Gaurav S Sukhatme


Granular materials are of critical interest to many robotic tasks in planetary science, construction, and manufacturing. However, the dynamics of granular materials are complex and often computationally very expensive to simulate. We propose a set of methodologies and a system for the fast simulation of granular materials on Graphics Processing Units (GPUs), and show that this simulation is fast enough for basic training with Reinforcement Learning algorithms, which currently require many dynamics samples to achieve acceptable performance. Our method models granular material dynamics using implicit timestepping methods for multibody rigid contacts, as well as algorithmic techniques for efficient parallel collision detection between pairs of particles and between particle and arbitrarily shaped rigid bodies, and programming techniques for minimizing warp divergence on Single-Instruction, Multiple-Thread (SIMT) chip architectures. We showcase our simulation system on several environments targeted toward robotic tasks, and release our simulator as an open-source tool.



    AUTHOR    = {David R Millard AND Daniel Pastor AND Joseph Bowkett AND Paul Backes AND Gaurav S Sukhatme}, 
    TITLE     = {{GranularGym: High Performance Simulation for Robotic Tasks with Granular Materials}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2023}, 
    ADDRESS   = {Daegu, Republic of Korea}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2023.XIX.034}