Abstract It is one of the great challenges for a robot to learn compliant movements in interaction tasks. The robot can easily acquire motion skills from a human tutor by kinematics demonstration, however, this becomes much more difficult when it comes to the compliant skills. This paper aims to provide a possible solution to address this problem by proposing a two-stage approach. In the first stage, the human tutor demonstrates the robot how to perform a task, during which only motion trajectories are recorded without the involvement of force sensing. A dynamical movement primitives (DMPs) model which can generate human-like motion is then used to encode the kinematics data. In the second stage, a biomimetic controller, which is inspired by the neuroscience findings in human motor learning, is employed to obtain the desired robotic compliant behaviors by online adapting the impedance profiles and the feedforward torques simultaneously. Several tests are conducted to validate the effectiveness of the proposed approach.

中文翻译：

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基于仿生运动适应学习顺应机器人运动

摘要 在交互任务中学习顺应动作是机器人面临的一大挑战。机器人可以通过运动学演示轻松地从人类导师那里获得运动技能，但是，当涉及到顺应技能时，这变得更加困难。本文旨在通过提出一个两阶段的方法来提供一种可能的解决方案来解决这个问题。在第一阶段，人类导师向机器人演示如何执行任务，在此期间只记录运动轨迹，不涉及力感知。然后使用可以生成类人运动的动态运动基元 (DMP) 模型对运动学数据进行编码。在第二阶段，仿生控制器，其灵感来自人类运动学习的神经科学发现，用于通过同时在线调整阻抗曲线和前馈扭矩来获得所需的机器人顺应行为。进行了多项测试以验证所提出方法的有效性。