Jaquier Noémie, Rozo Leonel, Caldwell Darwin G, Calinon Sylvain
Idiap Research Institute, Martigny, Switzerland.
Bosch Center for Artificial Intelligence, Renningen, Germany.
Int J Rob Res. 2021 Feb;40(2-3):624-650. doi: 10.1177/0278364920946815. Epub 2020 Aug 24.
Body posture influences human and robot performance in manipulation tasks, as appropriate poses facilitate motion or the exertion of force along different axes. In robotics, manipulability ellipsoids arise as a powerful descriptor to analyze, control, and design the robot dexterity as a function of the articulatory joint configuration. This descriptor can be designed according to different task requirements, such as tracking a desired position or applying a specific force. In this context, this article presents a novel framework, a method that allows robots to learn and reproduce manipulability ellipsoids from expert demonstrations. The proposed learning scheme is built on a tensor-based formulation of a Gaussian mixture model that takes into account that manipulability ellipsoids lie on the manifold of symmetric positive-definite matrices. Learning is coupled with a geometry-aware tracking controller allowing robots to follow a desired profile of manipulability ellipsoids. Extensive evaluations in simulation with redundant manipulators, a robotic hand and humanoids agents, as well as an experiment with two real dual-arm systems validate the feasibility of the approach.
身体姿势会影响人类和机器人在操作任务中的表现，因为合适的姿势有助于沿不同轴进行运动或施加力。在机器人技术中，可操作度椭球作为一种强大的描述符出现，用于根据关节配置分析、控制和设计机器人的灵巧性。该描述符可根据不同的任务要求进行设计，例如跟踪期望位置或施加特定力。在此背景下，本文提出了一种新颖的框架，即一种允许机器人从专家示范中学习并重现可操作度椭球的方法。所提出的学习方案基于高斯混合模型的张量形式，该模型考虑到可操作度椭球位于对称正定矩阵的流形上。学习与几何感知跟踪控制器相结合，使机器人能够跟踪期望的可操作度椭球轮廓。使用冗余机械手、机器人手和类人机器人进行的大量仿真评估，以及对两个真实双臂系统的实验验证了该方法的可行性。
