Behavior Learning of Human-Friendly Robots by Symbolic Teaching*

This paper deals with behavior learning of human-friendly robots by human symbolic teaching. The mobile robot has an internal model for its behavior criteria and acquires human teaching model based on the behav- ior criteria. Outputs of human teaching model are used for learning reactive motions such as collision avoidance behavior. The feature of this method is to obtain suitable behaviors through the interaction with environment and symbolic teaching by human intuition. Experimental results show that the robot can acquire collision avoidance behaviors through the interaction with human symbolic teaching in a given environment. UMAN-FRIENDLY robots are required in various fields including service industry and welfare. Re- cently, various methodologies for robotic control have been discussed in subsumption architecture, behavior-based robotics, and evolutionary robotics (1)-(7). These con- cepts are based on reactions that living creatures present. Robot's reactions can be described by production rules, neural networks, and fuzzy inference rules, which are ac- quired by learning in environments. However, a human- friendly robot should acquire its behaviors through inter- action with human in a given environment. Furthermore, the evaluations of human concerning the robot's behav- iors are different among human operators. This means that a human-friendly robot should acquire behaviors suit- able to a certain human operator. In this study, we dis- cuss a learning method for human-friendly robots. The learning methods can be classified into three types: super- vised learning, unsupervised learning, and reinforcement learning only with the response of success or failure (8)- (12). If a human operator can give exact teaching data, the robot can acquire a behavior suitable to the human opera- tor. However, it is difficult for the human operator to rep- resent exact numerical teaching data. Actually, symbolic communication such as "turn right," "go up," and "stop" is often used in teaching among human operators. In such a case, the robot must understand the meanings of sym- bolic teaching data. Therefore, the robot must build the human teaching model by itself. Here we use symbolic communication to share information between the robot and human operator. Therefore, the robot requires a mapping method from human symbolic information into numerical information for learning various behaviors based on human