Abstract
Reacting according to external sounds is an important ability in multi-robot and human-robot collaboration. Although network might be the first choice to connect multi-agents in the robot world, the unexpected connection snap would be a disaster to the whole system. Utilizing sounds is a feasible and supplementary way to transfer information between agents, which is also a smart and robust way to support swarm intelligence. In this paper, under the scenario of a robot band, the issue how each robot member achieves its performance ability is focused. Unlike most of the previous researches, we emphasize that robot’s performance ability is achieved all by itself in an autonomous way. And an approach of Listening-Playing Loop (LPL) is proposed, where the developmental learning is involved. With a simple drumming robot, the proposed approach is evaluated. Experimental results show the proposed approach is effective, and via transferring raw audio data to the motion control, the robot successfully develops the drumming ability.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bretan, M., Weinberg, G.: A survey of robotic musicianship. Commun. ACM 59(5), 100–109 (2016)
Onsetsds - real time musical onset detection c/c++ library. http://onsetsds.sourceforge.net
Ribes, A., Cerquides, J., Demiris, Y., de Mántaras, R.L.: Active learning of object and body models with time constraints on a humanoid robot. IEEE Trans. Cogn. Dev. Syst. 8(1), 26–41 (2016)
Weinberg, G., Driscoll, S., Parry, M.: Haile-an interactive robotic percussionist. In: ICMC (2005)
Kotosaka, S., Schaal, S.: Synchronized robot drumming by neural oscillator. J. Rob. Soc. Japan 19(1), 116–123 (2001)
Crick, C., Munz, M., Scassellati, B.: Synchronization in social tasks: robotic drumming. In: The 15th IEEE International Symposium on Robot and Human Interactive Communication, ROMAN 2006, pp. 97–102. IEEE (2006)
Arsenic, A.: Developmental learning on a humanoid robot. In: Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, vol. 4, pp. 3167–3172. IEEE (2004)
Sigaud, O., Droniou, A.: Towards deep developmental learning. IEEE Trans. Cogn. Dev. Syst. 8(2), 99–114 (2016)
Bello, J.P., Daudet, L., Abdallah, S., Duxbury, C., Davies, M., Sandler, M.B.: A tutorial on onset detection in music signals. IEEE Trans. Speech Audio Process. 13(5), 1035–1047 (2005)
Stowell, D., Plumbley, M.: Adaptive whitening for improved real-time audio onset detection. In: Proceedings of the 2007 International Computer Music Conference, ICMC 2007, pp. 312–319 (2007)
Luo, D., Hu, F., Deng, Y., Liu, W., Wu, X.: An infant-inspired model for robot developing its reaching ability. In: IEEE International Conference on Developmental Learning and Epigenetic Robotics, pp. 1–8 (2016)
Acknowledgement
The work is supported in part by the National Natural Science Foundation of China (No. 11590773, No. 61421062), the Key Program of National Social Science Foundation of China (No. 12 & ZD119) and the National Basic Research Program (973 Program) of China (No. 2013CB329304).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Wu, X., Liu, T., Deng, Y., Wu, X., Luo, D. (2017). Developing Robot Drumming Skill with Listening-Playing Loop. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10386. Springer, Cham. https://doi.org/10.1007/978-3-319-61833-3_59
Download citation
DOI: https://doi.org/10.1007/978-3-319-61833-3_59
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61832-6
Online ISBN: 978-3-319-61833-3
eBook Packages: Computer ScienceComputer Science (R0)