Abstract
Hand-coding locomotion controllers for modular robots is difficult due to their polymorphic nature. Instead, we propose to use a simple and distributed reinforcement learning strategy. ATRON modules with identical controllers can be assembled in any configuration. To optimize the robot’s locomotion speed its modules independently and in parallel adjust their behavior based on a single global reward signal. In simulation, we study the learning strategy’s performance on different robot configurations. On the physical platform, we perform learning experiments with ATRON robots learning to move as fast as possible. We conclude that the learning strategy is effective and may be a practical approach to design gaits.
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References
Bongard, J., Zykov, V., Lipson, H.: Resilient machines through continuous self-modeling. Science 314(5802), 1118–1121 (2006)
Christensen, D.J., Schultz, U.P., Brandt, D., Stoy, K.: A unified simulator for self-reconfigurable robots. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (2008)
Kamimura, A., Kurokawa, H., Yoshida, E., Murata, S., Tomita, K., Kokaji, S.: Automatic locomotion design and experiments for a modular robotic system. IEEE/ASME Transactions on Mechatronics 10(3), 314–325 (2005)
Lipson, H., Pollack, J.B.: Automatic design and manufacture of robotic lifeforms. Nature 406, 974–978 (2000)
Maes, P., Brooks, R.A.: Learning to coordinate behaviors. In: National Conference on Artificial Intelligence, pp. 796–802 (1990)
Marbach, D., Ijspeert, A.J.: Co-evolution of configuration and control for homogenous modular robots. In: Proc. 8th Int. Conf. on Intelligent Autonomous Systems, Amsterdam, Holland, pp. 712–719 (2004)
Marbach, D., Ijspeert, A.J.: Online Optimization of Modular Robot Locomotion. In: Proceedings of the IEEE Int. Conference on Mechatronics and Automation (ICMA 2005), pp. 248–253 (2005)
Mataric, M.J.: Reinforcement learning in the multi-robot domain. Auton. Robots 4(1), 73–83 (1997)
Sun Microsystems. Sun spot project, http://www.sunspotworld.com/
Østergaard, E.H., Kassow, K., Beck, R., Lund, H.H.: Design of the atron lattice-based self-reconfigurable robot. Auton. Robots 21(2), 165–183 (2006)
Sims, K.: Evolving 3d morphology and behavior by competition. In: Brooks, R., Maes, P. (eds.) Proc. Artificial Life IV, pp. 28–39. MIT Press, Cambridge (1994)
Smith, R.: Open dynamics engine (2005), www.ode.org
Sproewitz, A., Moeckel, R., Maye, J., Ijspeert, A.: Learning to move in modular robots using central pattern generators and online optimization. Int. J. Rob. Res. 27(3-4), 423–443 (2008)
Sutton, R.S., Barto, A.G.: Reinforcement Learning - An Introduction. MIT Press, Cambridge (1998)
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Christensen, D.J., Bordignon, M., Schultz, U.P., Shaikh, D., Stoy, K. (2009). Morphology Independent Learning in Modular Robots. In: Asama, H., Kurokawa, H., Ota, J., Sekiyama, K. (eds) Distributed Autonomous Robotic Systems 8. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00644-9_34
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DOI: https://doi.org/10.1007/978-3-642-00644-9_34
Publisher Name: Springer, Berlin, Heidelberg
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