Physical human-robot interactions (pHRIs) can improve robot autonomy and reduce physical demands on humans. In this paper, we consider a collaborative task with a considerably long object and no prior knowledge of the object's parameters. An integrated control framework with an online object parameter estimator and a Cartesian object-aware impedance controller is proposed to realize complicated scenarios. During the transportation task, the object parameters are estimated online while a robot and human lift an object. The perturbation motion is incorporated into the null space of the desired trajectory to enhance the estimator accuracy. An object-aware impedance controller is designed using the real-time estimation results to effectively transmit the intended human motion to the robot through the object. Experimental demonstrations of collaborative tasks, including object transportation and assembly tasks, are implemented to show the effectiveness of our proposed method.

翻译：物理人-机交互（pHRIs）可提升机器人自主性并降低人体体力消耗。本文针对无先验参数的长尺寸物体协作任务，提出一种集成在线物体参数估计器与笛卡尔空间物体感知阻抗控制器的联合控制框架，以应对复杂作业场景。在搬运任务中，机器人与人类共同抬举物体时在线估计物体参数，同时将扰动运动融入期望轨迹的零空间以提高估计精度。基于实时估计结果设计物体感知阻抗控制器，通过物体有效传递人类意图运动至机器人。通过包含物体搬运与装配任务的协作实验，验证了所提方法的有效性。