Social imitation learning is an essential skill that humans use to achieve social acceptance, increase awareness in unknown situations or to achieve cultural adaptation. In this work we address the problem of social imitation learning in a many-to-one learning scheme (group of humans to robot), where humans do not necessarily have teaching roles. Contrary to common imitation learning approaches based on one-to-one learning schemes with two agents (human teacher and robot student), our approach is inspired by social learning theory and consists in performing human behavior modeling by observing multiple humans while discovering common behavioral patterns. We propose a common framework for social behavior feature extraction that can be used to collect essential information of various social behaviors such as multi-person trajectory and multiple-body pose. Considering the fact that social imitation learning is shaped by stimuli of others’ behavior and the more individuals define the behavior, the more likely to engage in it; our modeling approach also considers a social force model that triggers social behavior learning when observing a group of people. Finally, collective behavior modeling is achieved by feature clustering using a Gaussian Mixture Model approach. Experimental results show that our approach is suitable for social human behavior modeling in situations such as emergency evacuation and Japanese style greeting (bowing).
Christian I. Penaloza, Yasushi Mae, Kenichi Ohara, and Tatsuo Arai: "Social Human Behavior Modeling for Robot Imitation Learning", Proceedings of 2012 IEEE International Conference on Mechatronics and Automation, pp.457-462, Chengdu, China, August 5-8, 2012.