A Robust Model Predictive Controller for Tactile Servoing

Tactile servoing is an effective approach to en- abling robots to safely interact with unknown environments. One of the core problems in tactile servoing is to robustly converge the contact features to the desired ones via a dedicated controller. This paper proposes a Data-Driven Model Predictive Controller (DDMPC) to compute the motion command given the previous interaction experience and feature deviations in tactile space. Compared with the manually designed PID-based controller, the proposed controller depends on the sound control theory and its convergence is guaranteed from a computational perspective. It is applied to the balancing control of a rolling bottle on a robotic forearm covered by a custom tactile sensor array. The real experiment demonstrates the superior robust- ness of the proposed approach and shows its great potential for other tactile servoing scenarios with measurement noise, which is inevitable for current tactile sensors.