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Skills transfer across dissimilar robots by learning context-dependent rewards
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
Robot programming by demonstration encompasses a wide range of learning strategies, from simple mimicking of the demonstrator's actions to the higher level extraction of the underlying intent. By focusing on this last form, we study the problem of extracting the reward function explaining the demonstrations from a set of candidate reward functions, and using this information for self-refinement of the skill. This definition of the problem has links with inverse reinforcement learning problemsdoi:10.1109/iros.2013.6696585 dblp:conf/iros/MalekzadehBCNC13 fatcat:ndqmlh3btfajjlijwiqzoxkz7i