Gaze control of humanoid robot for learning from demonstration

Abstract

Robots can learn knowledge by observing demonstration of humans. As tutees, robots need to not only observe human behaviors, but also make proper feedbacks for human tutors because learning is an interactive process in which information is delivered in bidirectional ways between humans and robots. Gaze is an adequate method for robots to provide human tutors with feedbacks that robots are concentrating on current learning because gaze directly represents where they are paying attention to. This paper proposes a gaze control algorithm with a state machine in learning from demonstration. A human tutor shows demonstration in front of a robot tutee, and the robot tutee observes the demonstration for learning. The robot tutee perceives external environment through its camera, recognizes a human and objects, and figures out a state at which the robot tutee is situated. Then, the robot tutee gazes at proper targets that are predefined by the state machine. The human tutor also adjusts the demonstration to make learning more effectively according to the robot tutee’s feedbacks. The effectiveness of the proposed method is demonstrated through the experiments with a robotic head with 17 degrees of freedom, developed in the RIT Lab., KAIST.