Nonprehensile Manipulation for Rapid Object Spinning via Multisensory Learning from Demonstration

Abstract

Dexterous manipulation concerns the control of a robot hand to manipulate an object in a desired manner. While classical dexterous manipulation strategies are based on stable grasping (or force closure), many human-like manipulation tasks do not maintain grasp stability and often utilize the dynamics of the object rather than the closed form of kinematic relation between the object and the robotic hand. Such manipulation strategies are referred as nonprehensile or dynamic dexterous manipulation in the literature. Nonprehensile manipulation often involves fast and agile movements such as throwing and flipping. Due to the complexity of such motions and uncertainties associated with them, it has been challenging to realize nonprehensile manipulation tasks in a reliable way. In this paper, we propose a new control strategy to realize practical nonprehensile manipulation. First, we make explicit use of multiple modalities of sensory data for the design of control law. Specifically, force data are employed for feedforward control, while position data are used for feedback control. Secondly, control signals (both feedback and feedforward) are obtained through multisensory learning from demonstration (LfD) experiments designed and performed for specific nonprehensile manipulation tasks of concern. To prove the concept of the proposed control strategy, experimental tests were conducted for a dynamic spinning task using a sensory-rich, two-finger robotic hand. The control performance (i.e., the speed and accuracy of the spinning task) was also compared with that of classical dexterous manipulation based on force closure and finger gaiting.