Abstract
A fast gait is an essential component of any successful team in the RoboCup 4-legged league. However, quickly moving quadruped robots, including those with learned gaits, often move in such a way so as to cause unsteady camera motions which degrade the robot’s visual capabilities. This paper presents an implementation of the policy gradient machine learning algorithm that searches for a parameterized walk while optimizing for both speed and stability. To the best of our knowledge, previous learned walks have all focused exclusively on speed. Our method is fully implemented and tested on the Sony Aibo ERS-7 robot platform. The resulting gait is reasonably fast and considerably more stable compared to our previous fast gaits. We demonstrate that this stability can significantly improve the robot’s visual object recognition.
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References
Rofer, T.: Evolutionary gait-optimization using a fitness function based on proprioception. In: Nardi, D., Riedmiller, M., Sammut, C., Santos-Victor, J. (eds.) RoboCup 2004. LNCS (LNAI), vol. 3276, pp. 310–322. Springer, Heidelberg (2005)
Kohl, N., Stone, P.: Machine learning for fast quadrupedal locomotion. In: The Nineteenth National Conference on Artificial Intelligence, pp. 611–616 (2004)
Kohl, N., Stone, P.: Policy gradient reinforcement learning for fast quadrupedal locomotion. In: Proceedings of the IEEE ICRA, IEEE Computer Society Press, Los Alamitos (2004)
Kim, M., Uther, W.: Automatic gait optimisation for quadruped robots. In: Australasian Conference on Robotics and Automation (2003)
Chernova, S., Veloso, M.: An evolutionary approach to gait learning for four-legged robots. In: Proceedings of IROS 2004 (2004)
Stronger, D., Stone, P.: A model-based approach to robot joint control. In: Nardi, D., Riedmiller, M., Sammut, C., Santos-Victor, J. (eds.) RoboCup 2004. LNCS (LNAI), vol. 3276, pp. 297–309. Springer, Heidelberg (2005)
Ng, A.Y., Coates, A., Diel, M., Ganapathi, V., Schulte1, J., Tse, B., Berger, E., Liang, E.: Autonomous helicopter flight via reinforcement learning. In: Advances in Neural Information Processing Systems 17, MIT Press, Advances in Neural Information Processing Systems (2004)
In, T.W., Vadakkepat, P.: Hybrid controller for biped gait generation. In: 2nd International Conference on Autonomous Robots and Agents (2004)
Hornby, G.S., Fujita, M., Takamura, S., Yamamoto, T., Hanagata, O.: Autonomous evolution of gaits with the sony quadruped robot. In: Genetic and Evolutionary Computation Conference, vol. 2, Morgan Kaufmann, San Francisco (1999)
Duffert, U., Hoffmann, J.: Reliable and precise gait modeling for a quadruped robot. In: Bredenfeld, A., Jacoff, A., Noda, I., Takahashi, Y. (eds.) RoboCup 2005. LNCS (LNAI), vol. 4020, Springer, Heidelberg (2006)
Rofer, T.: Germanteam robocup 2005. Technical report (2005)
Sony: Aibo robot (2005)
Stone, P., et al.: The UT Austin Villa 2004 RoboCup four-legged team: Coming of age. Technical Report UT-AI-TR-04-313, The University of Texas at Austin, Department of Computer Sciences, AI Laboratory (2004)
Sumengen, B., Manjunath, B.S., Kenney, C.: Image segmentation using multi-region stability and edge strength. In: The IEEE International Conference on Image Processing (ICIP) (2003)
Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 603–619 (2002)
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Saggar, M., D’Silva, T., Kohl, N., Stone, P. (2007). Autonomous Learning of Stable Quadruped Locomotion. In: Lakemeyer, G., Sklar, E., Sorrenti, D.G., Takahashi, T. (eds) RoboCup 2006: Robot Soccer World Cup X. RoboCup 2006. Lecture Notes in Computer Science(), vol 4434. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74024-7_9
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DOI: https://doi.org/10.1007/978-3-540-74024-7_9
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