This study investigates learning from demonstration (LfD) for contact-rich tasks. The procedure for choosing a task frame to express the learned signals for the motion and interaction wrench is often omitted or using expert insight. This article presents a procedure to derive the optimal task frame from motion and wrench data recorded during the demonstration. The procedure is based on two principles that are hypothesized to underpin the control configuration targeted by an expert, and assumes task frame origins and orientations that are fixed to either the world or the robot tool. It is rooted in screw theory, is entirely probabilistic and does not involve any hyperparameters. The procedure was validated by demonstrating several tasks, including surface following and manipulation of articulated objects, showing good agreement between the obtained and the assumed expert task frames. To validate the performance of the learned tasks by a UR10e robot, a constraint-based controller was designed based on the derived task frames and the learned data expressed therein. These experiments showed the effectiveness and versatility of the proposed approach. The task frame derivation approach fills a gap in the state of the art of LfD, bringing LfD for contact-rich tasks closer to practical application.

翻译：本研究探讨了面向富含接触任务的示范学习（LfD）。在表达运动与交互力旋的学习信号时，任务帧的选择过程常被省略或依赖专家经验。本文提出一种基于示范过程中记录的运动与力旋数据推导最优任务帧的方法。该方法基于两个假设性原理，这些原理被认为支撑了专家所针对的控制配置，并假设任务帧的原点与方向固定于世界坐标系或机器人工具坐标系。该方法植根于旋量理论，完全基于概率模型且无需任何超参数。通过演示包括表面跟踪与铰接物体操作在内的多项任务对方法进行验证，结果表明所获得的任务帧与假定的专家任务帧具有良好一致性。为验证UR10e机器人对学习任务的执行性能，我们基于推导出的任务帧及其中表达的学习数据设计了约束控制器。实验表明所提方法具有有效性与普适性。该任务帧推导方法填补了LfD领域的技术空白，推动面向富含接触任务的LfD更接近实际应用。