Abstract
Sharing reusable knowledge among robots has the potential to sustainably develop robot skills. The bottlenecks to sharing robot skills across a network are how to recognise and represent reusable robot skills in real-time and how to define reusable robot skills in a way that facilitates the recognition and representation challenge. In this paper, we first analyse the considerations to categorise reusable robot skills that manipulate objects derived from R.C. Schank’s script representation of human basic motion, and define three types of reusable robot skills on the basis of the analysis. Then, we propose a method with potential to identify robot skills in real-time. We present a theoretical process of skills recognition during task performance. Finally, we characterise reusable robot skill based on new definitions and explain how the new proposed representation of robot skill is potentially advantageous over current state-of-the-art work.
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References
Castro-González, Á., Malfaz, M., Salichs, M.A.: Learning the Selection of Actions for an Autonomous Social Robot by Reinforcement Learning Based on Motivations. International Journal of Social Robotics 3(4), 427–441 (2011)
Weng, J., McClelland, J., Pentland, A., Sporns, O., Stockman, I., Sur, M., Thelen, E.: Autonomous Mental Development by Robots and Animals. Science 291, 599–600 (2001)
Tenorth, M., Perzylo, A., Lafrenz, R., Beetz, M.: The RoboEarth Language: Representing and Exchanging Knowledge about Action, Objects and Environments. In: ICRA, pp. 1284–1289 (2012)
Cohen, P.R., Chang, Y.-H., Morrison, C.T.: Learning and Transferring Action Schemas. In: IJCAI, pp. 720–725 (2007)
Konidaris, G.D., Kuindersma, S.R., Grupen, R.A., Barto, A.G.: Robot Learning from Demonstration by Constructing Skill Trees. International Journal of Robotics Research 31(3), 360–375 (2012)
Mugan, J., Kuipers, B.: Autonomous Learning of High-Level States and Actions in Continuous Environments. IEEE Transactions on Autonomous Mental Development 4(1), 70–86 (2012)
Zhang, Y.L., Weng, J.Y.: Task Transfer by a Developmental Robot. IEEE Transactions on Evolutionary Computation 11(2), 226–248 (2007)
Piaget, J.: The Construction of Reality in the Child, New York (1954)
Schank, R., Abelson, R.P.: Scripts, Plans, Goals and Understanding: An Inquiry into Human Knowledge Structures. Erlbaum (1977)
Wiering, M., Otterlo, M.V.: Transfer in Reinforcement Learning Reinforcement Learning State-of-the-Art., ch. 5. Springer, Berlin (2012)
Konidaris, G.D., Kuindersma, S.R., Grupen, R.A., Barto, A.G.: Autonomous Skill Acquisition on a Mobile Manipulator. In: AAAI, pp. 1468–1473 (2011)
Sutton, R.S., Precup, D., Singh, S.: Between MDPs and semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning. Artificial Intelligence 112(1-2), 181–211 (1999)
Nicolescu, M.N., Matari, M.J.: A Hierarchical Architecture for Behavior-Based Robots. In: Proceedings of the First International Joint Conference on Autonomous Agents and Multi-agent Systems: Part 1. ACM, Bologna (2002)
Allen, J.F.: Maintaining Knowledge about Temporal Intervals. Communications of the ACM 26(11), 832–843 (1983)
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Wang, W., Johnston, B., Williams, MA. (2013). Recognition and Representation of Robot Skills in Real Time: A Theoretical Analysis. In: Herrmann, G., Pearson, M.J., Lenz, A., Bremner, P., Spiers, A., Leonards, U. (eds) Social Robotics. ICSR 2013. Lecture Notes in Computer Science(), vol 8239. Springer, Cham. https://doi.org/10.1007/978-3-319-02675-6_13
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DOI: https://doi.org/10.1007/978-3-319-02675-6_13
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