This paper explores the problem of collision-free motion generation for manipulators by formulating it as a global motion optimization problem. We develop a parallel optimization technique to solve this problem and demonstrate its effectiveness on massively parallel GPUs. We show that combining simple optimization techniques with many parallel seeds leads to solving difficult motion generation problems within 50ms on average, 60x faster than state-of-the-art (SOTA) trajectory optimization methods. We achieve SOTA performance by combining L-BFGS step direction estimation with a novel parallel noisy line search scheme and a particle-based optimization solver. To further aid trajectory optimization, we develop a parallel geometric planner that plans within 20ms and also introduce a collision-free IK solver that can solve over 7000 queries/s. We package our contributions into a state of the art GPU accelerated motion generation library, cuRobo and release it to enrich the robotics community. Additional details are available at https://curobo.org

翻译：本文通过将机械臂无碰撞运动生成问题建模为全局运动优化问题，探索了该问题的求解方法。我们提出了一种并行优化技术，并在大规模并行GPU上验证其有效性。研究表明，将简单的优化技术与大量并行种子相结合，可在平均50毫秒内解决复杂的运动生成问题，速度比最先进的轨迹优化方法提升60倍。通过将L-BFGS步长方向估计与新颖的并行噪声线搜索方案及基于粒子群优化求解器相结合，我们达到了当前最优性能。为进一步辅助轨迹优化，我们开发了可在20毫秒内完成规划的并行几何规划器，并引入可每秒求解7000余次查询的无碰撞逆运动学求解器。我们将这些研究成果整合为当前最先进的GPU加速运动生成库cuRobo并予以开源，以促进机器人社区发展。更多详情请访问https://curobo.org。