Stability and self-organization of cooperative motions in multi-fingered robot hands

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Intelligent control of multiple-robots in cooperative motions requires sophisticated inter-arm andinter-finger coordination. While living creatures offer proof-of-concept, our understanding of how it can be realized in controlling robotic mechanisms is not complete. The main research goals were directed to understanding and realization of mechanisms for generating stable coordinated motion patterns, and also to developing principles of self-organization of cooperative motions. In the first part we studied configuration and force-dependent stability. To guarantee the system stability, one needs to understand how the arm compliance and regulation of the internal forces in musculo-skeletal system can be used in building stable motion patterns. Decomposing the motion and force redundancy, we have found some necessary and sufficient stability conditions for the internal force distributions and established criteria of stabilizability that can be used in designing impedance control schemes. In the second part we were dealing with decentralized coordination strategy and cooperative control. To avoid the complexity of the centralized control, we have developed a genetic-based machine learning technique, with reinforcement signals built upon the stability criteria. Initial experiments show the feasibility of this concept. Finally, we have studied a force distribution problem and found an optimality criterion for natural, human-like force generation.