Abstract
In this paper, we investigate an approach for robots to extract the preferences of human observers, and combine them to generate new moves in order to improve robot dancing. Human preferences can be extracted even when a reward is given a few steps after a dance movement. With the feedback the robots perform more of what was preferred and less of what was not preferred. Human observers watch the robot generated dance movements and provide feedback in real time; then the robot learns the observers’ preferences and creates new dance movements based on varying percentage of their preferences; and finally the observers rate the new robot’s dancing. Experimental results show that the robot learns, using Interactive Reinforcement Learning, the expressed preferences of human observers and dance routines based on preferences of multiple observers are rated more highly.
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References
Aucouturier, J.J.: Dancing robots and AI’s future. IEEE Intelligent Systems 23(2), 74–84 (2008)
Santiago, C., Oliveira, J., Reis, L., Sousa, A.: Autonomous robot dancing synchronized to musical rhythmic stimuli. In: 2011 6th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1–6 (2011)
Shiratori, T., Ikeuchi, K.: Synthesis of dance performance based on analyses of human motion and music. IPSJ Transactions on Computer Vision and Image Media 1(1), 34–47 (2008)
Jens, H., Peer, A., Buss, M.: Synthesis of an interactive haptic dancing partner. Control, 527–532 (2010)
Solis, J., Chida, K., Suefuji, K., Takanishi, A.: Improvements of the sound perception processing of the anthropomorphic flutist robot (WF-4R) to effectively interact with humans. In: IEEE International Workshop on Robot and Human Interactive Communication, pp. 450–455 (2005)
Tanaka, F., Movellan, J.R., Fortenberry, B., Aisaka, K.: Daily HRI evaluation at a classroom environment: Reports from dance interaction experiments. In: Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-Robot Interaction, pp. 3–9 (2006)
Vircikova, M., Sincak, P.: Dance choreography design of humanoid robots using interactive evolutionary computation. In: 3rd Workshop for Young Researchers: Human Friendly Robotics for Young Researchers (2010)
Dozier, G.: Evolving robot behavior via interactive evolutionary computation: From real-world to simulation. In: Proceedings of the 2001 ACM Symposium on Applied Computing, pp. 340–344 (2001)
Thomaz, A.L., Hoffman, G., Breazeal, C.: Real-time interactive reinforcement learning for robots. In: AAAI 2005 Workshop on Human Comprehensible Machine Learning (2005)
Thomaz, A., Breazeal, C.: Asymmetric interpretations of positive and negative human feedback for a social learning agent. In: The 16th IEEE International Symposium on Robot and Human Interactive Communication, pp. 720–725 (2007)
Sutton, R., Barto, A.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Cyberbotics: Webots 6 for fast prototyping and simulation of mobile robots (2011), http://www.cyberbotics.com
Tholley, I.: Towards A Framework To Make Robots Learn To Dance. PhD thesis, Loughborough University, UK (2012)
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Meng, Q., Tholley, I., Chung, P.W.H. (2012). Robot Dancing: Adapting Robot Dance to Human Preferences. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34500-5_66
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DOI: https://doi.org/10.1007/978-3-642-34500-5_66
Publisher Name: Springer, Berlin, Heidelberg
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