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Object Affordance Driven Inverse Reinforcement Learning Through Conceptual Abstraction and Advice
2018
Paladyn: Journal of Behavioral Robotics
Within human Intent Recognition (IR), a popular approach to learning from demonstration is Inverse Reinforcement Learning (IRL). IRL extracts an unknown reward function from samples of observed behaviour. Traditional IRL systems require large datasets to recover the underlying reward function. Object affordances have been used for IR. Existing literature on recognizing intents through object affordances fall short of utilizing its true potential. In this paper, we seek to develop an IRL system
doi:10.1515/pjbr-2018-0021
fatcat:m6a2fm5ja5elnp25prgy6mnjp4