用于解释接触式物体操作任务的运动与力轨迹不变描述子
Invariant Descriptors of Motion and Force Trajectories for Interpreting Object Manipulation Tasks in Contact
AI总结：
针对接触任务缺乏力轨迹不变表示的问题，本文利用运动与力的对偶性提出两类不变描述子，并基于最优控制实现含噪测量下的鲁棒计算，在三维轮廓跟踪和轴孔对齐演示中验证其坐标系与传感器位置不变性。
摘要：
过去，点和刚体运动轨迹的不变描述子已被提出，作为用于运动识别与泛化的代表性任务模型。目前，尚不存在用于表示接触任务中出现的力轨迹的不变描述子。本文利用运动与力之间的对偶性，引入了力轨迹的不变描述子。根据轨迹由旋量坐标还是向量坐标组成，文中提出了两类不变描述子。文中提供了利用最优控制从含噪测量中鲁棒计算不变描述子的方法和软件。通过对三维轮廓跟踪和轴孔对齐任务的人类演示实验，不变描述子被证明能够产生不依赖于参考坐标系标定或传感器位置的任务表示。最优控制问题的整定过程被证明快速且直观。与自由空间中的运动类似，所提出的运动和力轨迹不变描述子可能被证明有助于约束运动的识别与泛化，例如接触式物体操作期间的约束运动。
英文摘要：
Invariant descriptors of point and rigid-body motion trajectories have been proposed in the past as representative task models for motion recognition and generalization. Currently, no invariant descriptor exists for representing force trajectories, which appear in contact tasks. This paper introduces invariant descriptors for force trajectories by exploiting the duality between motion and force. Two types of invariant descriptors are presented depending on whether the trajectories consist of screw or vector coordinates. Methods and software are provided for robustly calculating the invariant descriptors from noisy measurements using optimal control. Using experimental human demonstrations of 3D contour following and peg-on-hole alignment tasks, invariant descriptors are shown to result in task representations that do not depend on the calibration of reference frames or sensor locations. The tuning process for the optimal control problems is shown to be fast and intuitive. Similar to motions in free space, the proposed invariant descriptors for motion and force trajectories may prove useful for the recognition and generalization of constrained motions, such as during object manipulation in contact.
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。
