グラニュラー・ジム：粒状材料を用いたロボットタスクのための高性能シミュレーション
Granular Gym: High Performance Simulation for Robotic Tasks with Granular Materials
GPU上で粒状材料の動力学を高速にシミュレーションする手法とシステムを提案し、強化学習の訓練に十分な速度を実現した。
著者: David Millard, Daniel Pastor, Joseph Bowkett, Paul Backes, Gaurav S. Sukhatme
分類: cs.RO
原文アブストラクト
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.
