Trajectory generation and accumulation for partner robots based on structured learning

The aim of This work is to develop partner robots that can obtain and accumulate human-friendly behaviors. To realize it, we use a concept of structured learning which emphasizes the importance of an interactive learning of several modules through interaction with its environment. In a proposed method, a robot obtains hand-to-hand behavior by using an interactive evolutionary computation based on human evaluations estimated by fuzzy state-value functions. Moreover, a self-organizing map is used for clustering human hand positions. A state-value function and a knowledge database are assigned to each clustered positions. Furthermore, the best trajectory is stored in the knowledge database to reuse it in the same situation. Some experimental results show the effectiveness of the proposed method.