The Extraction of Symbolic Postures to Transfer Social Cues into Robot

Abstract

In this paper, we presented a framework to transfer the natural gestural behaviors of a human agent to a robot through a robust imitation algorithm. The novelty of our proposed algorithm is the use of symbolic postures to generate the gestural behaviors of a robot ithout using any training data or trained model. The idea behind using symbolic postures is that a robot is flexibly able to generate its own motion. The main challenge in robot imitation is identifying the changing points of motion direction at each time interval. In our approach, we estimated the changing points of motion direction through posture dissimilarity values and reinforcement learning at each time interval. The image processing-based method obtained some noisy data that estimated the position of the colored patches. The noisy data did not have a significant effect on the accurate generation of the robot's motion, which was due to the fact that the imitation algorithm generated the robot's motion through only a small number of symbolic postures. Overall, the experimental results revealed that the proposed imitation algorithm imitated the human gestural behaviors quite accurately, except during only a few time intervals.