Teaching robots how to apply forces according to our preferences is still an open challenge that has to be tackled from multiple engineering perspectives. This paper studies how to learn variable impedance policies where both the Cartesian stiffness and the attractor can be learned from human demonstrations and corrections with a user-friendly interface. The presented framework, named ILoSA, uses Gaussian Processes for policy learning, identifying regions of uncertainty and allowing interactive corrections, stiffness modulation and active disturbance rejection. The experimental evaluation of the framework is carried out on a Franka-Emika Panda in four separate cases with unique force interaction properties: 1) pulling a plug wherein a sudden force discontinuity occurs upon successful removal of the plug, 2) pushing a box where a sustained force is required to keep the robot in motion, 3) wiping a whiteboard in which the force is applied perpendicular to the direction of movement, and 4) inserting a plug to verify the usability for precision-critical tasks in an experimental validation performed with non-expert users.
翻译:教授机器人如何按照我们的偏好运用力量,仍是一个公开的挑战,需要从多种工程角度来应对。本文研究如何学习可变阻力政策,这样可以用用户友好的界面从人类的演示和校正中学习笛卡尔的僵硬和吸引器。介绍的框架名为ILOSA,使用高山进程进行政策学习,确定不确定区域,允许互动校正、僵硬调制和主动扰动拒绝。框架的实验性评估是在弗朗卡-埃米卡潘达的4个不同案例中进行的,具有独特的武力互动特性:1) 拔出一个插头,在成功清除插头时突然出现不连续的阻力;2) 推动一个盒子,需要持续的力量来保持机器人的动;3) 擦一个白板,在白板上将力量用于运动方向,以及4) 插入一个插头,以核查非专家用户在实验性验证中完成的精确任务是否可行。