Human like learning algorithm for simultaneous force control and haptic identification

This paper develops a learning control algorithm adapting the reference point and force to interact with an object of unknown geometry and elasticity. The controller is inspired by neuroscience studies that investigated the neural mechanisms when human adapt to virtual objects of different properties. The learning control algorithm estimates the shape and stiffness of the given object while maintaining a specified contact force with the environment. Simulations demonstrate the efficiency of the algorithm to identify the geometry and impedance of an unknown object without requiring force sensing. These properties are attractive for robotic haptic exploration with little demand on the sensing.