Policy Adaptation through Tactile Correction

Demonstration learning is a powerful and practical technique to develop robot behaviors. Even so, development remains a challenge and possible demonstration limitations, for example correspondence issues between the robot and demonstrator, can degrade policy performance. This work presents an approach for policy improvement through a tactile interface located on the body of the robot. We introduce the Tactile Policy Correction (TPC) algorithm, that employs tactile feedback for the refinement of a demonstrated policy, as well as its reuse for the development of other policies.


Published in:
Thirty Sixth Annual Convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB’10)
Presented at:
Thirty Sixth Annual Convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB’10), Leicester, United Kingdom, March, 2010
Year:
2010
Laboratories:




 Record created 2010-03-10, last modified 2018-09-13

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