基于接触反馈和GPU加速机器人仿真的手持物体位姿跟踪
In-Hand Object Pose Tracking via Contact Feedback and GPU-Accelerated Robotic Simulation
摘要：
由于显著的遮挡，跟踪被机器人手握持和操纵的对象的姿态对于基于视觉的方法是困难的。先前的工作已经探索了使用接触反馈和粒子滤波器来定位手中的对象。然而，他们主要集中在静态抓取设置上，而不是在物体运动时，因为这样做需要对复杂的接触动力学进行建模。在这项工作中，我们建议使用GPU加速的并联机器人模拟和导数免费的，基于样本的优化跟踪在手对象的姿态与接触反馈在操作过程中。我们使用物理仿真作为机器人与物体交互的前向模型，该算法对仿真的状态和参数进行联合优化，使它们更好地与现实世界的状态和参数相匹配。该方法在单GPU上实时运行（30Hz），模拟实验中点云距离平均误差为6mm，真实环境中点云距离平均误差为13mm。在https://sites.google.com/view/in-hand-object-pose-tracking/上观看实验视频
英文摘要：
Tracking the pose of an object while it is being held and manipulated by a robot hand is difficult for vision-based methods due to significant occlusions. Prior works have explored using contact feedback and particle filters to localize in-hand objects. However, they have mostly focused on the static grasp setting and not when the object is in motion, as doing so requires modeling of complex contact dynamics. In this work, we propose using GPU-accelerated parallel robot simulations and derivative-free, sample-based optimizers to track in-hand object poses with contact feedback during manipulation. We use physics simulation as the forward model for robot-object interactions, and the algorithm jointly optimizes for the state and the parameters of the simulations, so they better match with those of the real world. Our method runs in real-time (30Hz) on a single GPU, and it achieves an average point cloud distance error of 6mm in simulation experiments and 13mm in the real-world ones. View experiment videos at https://sites.google.com/view/in-hand-object-pose-tracking/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
