A Self-Tuning Impedance Controller for Autonomous Robotic Manipulation

Complex interactions with unstructured environments require the application of appropriate restoring forces in response to the imposed displacements. Impedance control techniques provide effective solutions to achieve this, however, their quasi-static performance is highly dependent on the choice of parameters, i.e. stiffness and damping. In most cases, such parameters are previously selected by robot programmers to achieve a desired response, which limits the adaptation capability of robots to varying task conditions. To improve the generality of interaction planning through task-dependent regulation of the parameters, this paper introduces a novel self-regulating impedance controller. The regulation of the parameters is achieved based on the robot's local sensory data, and on an interaction expectancy value. This value combines the interaction values from the robot state machine and visual feedback, to authorize the autonomous tuning of the impedance parameters in selective Cartesian axes. The effectiveness of the proposed method is validated experimentally in a debris removal task.