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 three 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, and 3) wiping a whiteboard in which the force is applied perpendicular to the direction of movement.

中文翻译：

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ILoSA：僵化和吸引子的交互式学习

教机器人如何根据我们的喜好施加力仍然是一个开放的挑战，必须从多个工程角度解决。本文研究了如何学习可变阻抗策略，通过人性化的演示和校正，可以使用人性化的界面来学习笛卡尔刚度和吸引子。提出的名为ILoSA的框架使用高斯过程进行政策学习，识别不确定性区域并允许交互式更正，刚度调制和主动干扰抑制。该框架的实验评估是在Franka-Emika Panda上的三种单独的情况下进行的，这些情况具有独特的力相互作用特性：1）拉动塞子，其中在成功移除塞子后会突然出现力不连续的情况，