Effective learning system techniques for human-robot interaction in service environment

HRI (Human-Robot Interaction) is often frequent and intense in assistive service environment and it is known that realizing human-friendly interaction is a very difficult task because of human presence as a subsystem of the interaction process. After briefly discussing typical HRI models and characteristics of human, we point out that learning aspect would play an important role for designing the interaction process of the human-in-the loop system. We then show that the soft computing toolbox approach, especially with fuzzy set-based learning techniques, can be effectively adopted for modeling human behavior patterns as well as for processing human bio-signals including facial expressions, hand/ body gestures, EMG and so forth. Two project works are briefly described to illustrate how the fuzzy logic-based learning techniques and the soft computing toolbox approach are successfully applied for human-friendly HRI systems. Next, we observe that probabilistic fuzzy rules can handle inconsistent data patterns originated from human, and show that combination of fuzzy logic, fuzzy clustering, and probabilistic reasoning in a single frame leads to an algorithm of iterative fuzzy clustering with supervision. Further, we discuss a possibility of using the algorithm for inductively constructing probabilistic fuzzy rule base in a learning system of a smart home. Finally, we propose a life-long learning system architecture for the HRI type of human-in-the-loop systems.