Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Believability testing and bayesian imitation in interactive computer games
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
Machine Learning With AIBO Robots in the Four-Legged League of RoboCup
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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The ambitious goal being pursued by researchers participating in the RoboCup challenge [8] is to develop a team of autonomous humanoid robots that is capable of winning against a team of human soccer players. An important step in this direction is to actively utilise human coaching to improve the skills of robots at both tactical and strategic levels. In this paper we explore the hypothesis that embedding a human into a robot's body and allowing the robot to learn tactical decisions by imitating the human coach can be more efficient than programming the robot explicitly. To enable this, we have developed a sophisticated HRI system that allows a human to interact with, coach and control an Aldebaran Nao robot through the use of a motion capture suit, portable computing devices (iPhone and iPad), and a head mounted display (which allows the human controller to experience the robot's visual perceptions of the world). This paper describes the HRI-Coaching system we have developed, detailing the underlying technologies and lessons learned from using it to control the robot. The system in its current stages shows high potential for human-robot coaching, but requires further calibration and development to allow a robot to learn by imitating the human coach.