Abstract
This paper describes an architecture that enables a robot to represent, reason about, and learn affordances. Specifically, Answer Set Prolog is used to represent and reason with incomplete domain knowledge that includes affordances modeled as relations between attributes of the robot and the object(s) in the context of specific actions. The learning of affordance relations from observations obtained through reactive execution or active exploration is formulated as a reinforcement learning problem. A sampling-based approach and decision-tree regression with the underlying relational representation are used to obtain generic affordance relations that are added to the Answer Set Prolog program for subsequent reasoning. The capabilities of this architecture are illustrated and evaluated in the context of a simulated robot assisting humans in an indoor domain.
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Notes
- 1.
We use the terms “robot”, “agent” and “learner” interchangeably in this paper.
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Acknowledgments
This work was supported in part by the US Office of Naval Research award N00014-13-1-0766 and Asian Office of Aerospace Research and Development award FA2386-16-1-4071. All conclusions are those of the authors alone.
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Sridharan, M., Meadows, B. (2017). Learning Affordances for Assistive Robots. In: Kheddar, A., et al. Social Robotics. ICSR 2017. Lecture Notes in Computer Science(), vol 10652. Springer, Cham. https://doi.org/10.1007/978-3-319-70022-9_1
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