This paper aims to enable the robot to obtain human-like compliant manipulation skills for the peg-in-hole (PiH) assembly task by learning from demonstration.,A modified dynamic movement primitives (DMPs) model with a novel hybrid force/position feedback in Cartesian space for the robotic PiH problem is proposed by learning from demonstration. To ensure a compliant interaction during the PiH insertion process, a Cartesian impedance control approach is used to track the trajectory generated by the modified DMPs.,The modified DMPs allow the robot to imitate the trajectory of demonstration efficiently and to generate a smoother trajectory. By taking advantage of force feedback, the robot shows compliant behavior and could adjust its pose actively to avoid a jam. This feedback mechanism significantly improves the dynamic performance of the interactive process. Both the simulation and the PiH experimental results show the feasibility and effectiveness of the proposed model.,The trajectory and the compliant manipulation skill of the human operator can be learned simultaneously by the new model. This method adopted a modified DMPs model in Cartesian space to generate a trajectory with a lower speed at the beginning of the motion, which can reduce the magnitude of the contact force.

中文翻译：

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使用具有反馈机制的笛卡尔 DMP 学习钉孔装配

本文旨在通过从演示中学习，使机器人能够获得类似人类的顺从操作技能，用于孔中钉 (PiH) 组装任务。具有新颖混合力/位置反馈的改进的动态运动基元 (DMP) 模型通过从演示中学习，提出了在笛卡尔空间中解决机器人 PiH 问题的方法。为了确保 PiH 插入过程中的顺应交互，使用笛卡尔阻抗控制方法来跟踪修改后的 DMP 生成的轨迹。修改后的 DMP 使机器人能够有效地模仿演示轨迹并生成更平滑的轨迹。通过利用力反馈，机器人表现出顺从的行为，并可以主动调整其姿势以避免卡住。这种反馈机制显着提高了交互过程的动态性能。仿真和PiH实验结果都表明了所提出模型的可行性和有效性。新模型可以同时学习人类操作者的轨迹和顺从操作技能。该方法在笛卡尔空间中采用改进的DMPs模型，在运动开始时生成速度较低的轨迹，可以减小接触力的大小。