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Document type:
Magazinartikel 
Author(s):
Darrin C Bentivegna, Christopher G Atkeson, Gordon Cheng 
Title:
Learning tasks from observation and practice 
Abstract:
This paper presents a framework that gives robots the ability to initially learn a task behavior from observing others. The framework includes a method for the robots to increase performance while operating in the task environment. We demonstrate this approach applied to air hockey and the marble maze task. Our robots initially learn to perform the tasks using learning from observation, and then increase their performance through practice. 
Journal title:
Robotics and Autonomous Systems 
Year:
2004 
Journal volume:
47 
Year / month:
2004-06 
Journal issue:
2-3 
Pages contribution:
163-169 
Reviewed:
ja 
Language:
en