Human-robot-contact-state identification based on tactile recognition

In this paper, we propose a method for designing an identification system for human-robot contact states based on tactile recognition. The following ideas are incorporated: experimentation for human-robot contact, verbalization of contact states, extraction of characteristic parameters from acquired tactile information, quantification of the recipient's tactile recognition incorporating its redundancy (identification confusability among contact states), evaluation of the identification confusability with a new criterion, and identification of contact states based on the received tactile stimulation. The proposed method allows a robot to quantify tactile recognition of a human (recipient) touched by other people (touch initiator), in which the verbal response by the recipient is matched with tactile stimulation acquired during physical contact utilizing a tactile interface. In addition, the method enables a robot that comes into contact with a human to identify contact states nearly similar to that of the recipient, based on the features of the received tactile stimulation. At this point, the reproduction of the identification confusability of the recipient's tactile recognition is also accomplished by using a neural network called modified counterpropagation (MCP). Once a tactile stimulation is induced on the robot body, the probability of corresponding contact states is calculated and outputted by the system, based on the degree of similarity of the characteristics between the newly received and previously stored tactile stimulation. Experimental results indicate that the constructed system allows a successful quantification of the recipient's contact-state recognition incorporating the identification confusability and the accomplishment of a high level of accuracy in contact-state identification. These results confirm that the proposed method is useful for identifying human-robot contact states based on tactile recognition.