DocumentCode :
3180424
Title :
Learning Similar Tasks From Observation and Practice
Author :
Bentivegna, Darrin C. ; Atkeson, Christopher G. ; Cheng, Gordon
Author_Institution :
Dept. of Humanoid Robotics & Comput. Neuroscience, ATR Comput. Neuroscience Lab., Kyoto
fYear :
2006
fDate :
9-15 Oct. 2006
Firstpage :
2677
Lastpage :
2683
Abstract :
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
Keywords :
behavioural sciences; learning (artificial intelligence); path planning; robots; behavioral primitives; learning; marble maze task; robot; subgoal generation policies; Education; Educational robots; Hardware; Humanoid robots; Intelligent robots; Laboratories; Libraries; Navigation; Orbital robotics; USA Councils;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on
Conference_Location :
Beijing
Print_ISBN :
1-4244-0258-1
Electronic_ISBN :
1-4244-0259-X
Type :
conf
DOI :
10.1109/IROS.2006.281989
Filename :
4058795
Link To Document :
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