Darrin C Bentivegna, Christopher G Atkeson, Gordon Cheng
Learning similar tasks from observation and practice
This paper presents a case study of learning to select behavioral primitives and generate subgoals from observation and practice. Our approach uses local features to generalize across tasks and global features to learn from practice. We demonstrate this approach applied to the marble maze task. Our robot uses local features to initially learn primitive selection and subgoal generation policies from observing a teacher maneuver a marble through a maze.
Book / Congress title:
2006 IEEE/RSJ International Conference on Intelligent Robots and Systems