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IROS
2006
IEEE

Learning Similar Tasks From Observation and Practice

9 years 7 months ago
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. The robot then uses this information as it tries to traverse another maze, and refines the information during learning from practice.
Darrin C. Bentivegna, Christopher G. Atkeson, Gord
Added 12 Jun 2010
Updated 12 Jun 2010
Type Conference
Year 2006
Where IROS
Authors Darrin C. Bentivegna, Christopher G. Atkeson, Gordon Cheng
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