Motion adaptation against environmental changes based on self-evaluation

We suggest motion adaptation method against environmental changes for service robots. Several motions such as walking, dancing, demonstration and so on are described with time series patterns. These motions are optimized with the architecture of the robot and under certain surrounding environment. Under unknown environment, robots cannot accomplish their tasks. We propose motion generation system based on heuristic search with histories of internal sensor values. New motion patterns are explored under unknown environment based on self-evaluation function. Self-evaluation function is composed of difference of internal sensor values between standard environment and unknown environment. New motion patterns are generated to maximize self-evaluation function without external information, such as run length, position of robot, human observation and so on. Experimental results show that the possibility to adapt autonomously patterned motions to environmental changes.