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A classifier system for the reinforcement learning control of autonomous mobile robots is proposed. The classifier system contains action selection, rules reproduction, and credit assignment mechanisms. An important feature of the classifier system is that it operates with continuous sensor and action spaces. The system is applied to the control of mobile robots. The local controllers use independent classifiers specified at the wheel-level. The controllers work autonomously, and with respect to each other represent dynamic systems connected through the external environment. The feasibility of the proposed system is tested in an experiment with a Khepera robot. It is shown that some patterns of global behavior can emerge from locally organized classifiers.

Recently, various prosthetic arms have been developed, but few are both attractive and functional. Considering human coexistence, prosthetic arms must be both safe and flexible. In this research, we developed a novel prosthetic arm with a five-fingered prosthetic hand using our original pneumatic actuators and a slender tendon-driven wrist using a wire drive and two small motors. Because the prosthetic hand's driving source is comprised of small pneumatic actuators, the prosthetic hand is safe when it makes contact with people; it can also operate flexibly. In addition, the arm has a tendon-driven wrist to expand its motion space and to perform many operations. First, we explain the pneumatic hand's drive mechanism and its tendon-driven wrist. Next, we identify the characteristics of the hand and the wrist and construct a control system for this arm and verify its control performance.

We present an artificial muscle-type pneumatic actuator as the driving source of a robot hand that is both safe and flexible. Some development of robot hands using pneumatic actuators has already taken place. However, in using a pneumatic actuator, a big compressor is needed. So the driving system also needs to be big, and enlargement of the driving system is a major problem.