A Self-Adaptive Robot Control Framework for Improved Tracking and Interaction Performances in Low-Stiffness Teleoperation

The improved adaptability of a robotic teleoperation system to unexpected disturbances in remote environments can be achieved by compliance control. Nevertheless, complying with all types of interaction forces while performing realistic manipulation tasks may deteriorate the teleoperation performance. For instance, the loading effect of the objects and tools that are held and manipulated by the robot can introduce undesired deviations from the reference trajectories in case of low-stiffness (or high payload) teleoperation. Although this can be addressed by updating the robot dynamics with the external loading effect, a sudden loss of the object may also generate undesired and potentially dangerous robot behaviours. To address this problem, we propose a novel and self-adaptive teleoperation framework. The method uses the feedback from robot's force sensors to recognize the interaction aspects that must be compensated by robot dynamics. Thanks to this online compensation, the slave robot reduces the tracking error with respect to the commanded motion by the human operator, while performing complex interactive tasks without the haptic feedback. The robot local controller also includes an energy tank based passivity paradigm to be able to manage unexpected collisions or a contact loss without resulting in an unsafe behaviour. We validate the proposed approach by experiments on a torque-controlled robotic arm performing manipulation tasks that require both object manipulation and environment Interaction.