Manipulability ellipsoids efficiently capture the human pose and reveal information about the task at hand. Their use in task-dependent robot teaching - particularly their transfer from a teacher to a learner - can advance emulation of human-like motion. Although in recent literature focus is shifted towards manipulability transfer between two robots, the adaptation to the capabilities of the other kinematic system is to date not addressed and research in transfer from human to robot is still in its infancy. This work presents a novel manipulability domain adaptation method for the transfer of manipulability information to the domain of another kinematic system. As manipulability matrices/ellipsoids are symmetric positive-definite (SPD) they can be viewed as points on the Riemannian manifold of SPD matrices. We are the first to address the problem of manipulability transfer from the perspective of point cloud registration. We propose a manifold-aware Iterative Closest Point algorithm (ICP) with parallel transport initialization. Furthermore, we introduce a correspondence matching heuristic for manipulability ellipsoids based on inherent geometric features. We confirm our method in simulation experiments with 2-DoF manipulators as well as 7-DoF models representing the human-arm kinematics.
翻译:有效捕捉人体表面并揭示手头任务的信息。 它们用于任务依赖的机器人教学, 特别是从教师向学习者转移, 可以推进模拟人文运动。 虽然最近文献的重点已转向两个机器人之间的人文迁移, 适应其他运动系统的能力的问题至今尚未解决, 而从人类向机器人的转移研究仍处于初级阶段。 这项工作为将可操作性信息转移到另一个运动系统领域提供了一种新的可操作性域适应方法。 由于可操作性矩阵/ elllidids 是对称正- 定律仪( SPD), 它们可以被视为SPD矩阵里曼多层的点点。 我们是第一个从点云登记角度解决可操作性转移问题的第一个。 我们提出了一个多维异性近点算算算法( ICP ), 并同时进行传输初始化。 此外, 我们引入了一种匹配人文可操作性模型( 7) 匹配的可操作性电子模型( ), 并证实作为人类内在的地球模型( II- F) 的模拟方法。