Bayesian gait optimization for bipedal locomotion

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8426 LNCS, Issue , 2014, Pages 274-290

  Calandra, Roberto ^a   Gopalan, Nakul ^b   Seyfarth, André ^a   Peters, Jan ^a,c   Deisenroth, Marc Peter ^a,d  

^a DARMSTADT UNIVERSITY OF TECHNOLOGY   (Germany)

^b Brown Univerity   (United States)

^c MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMS   (Germany)

^d IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

Bayesian optimization; Bipedal locomotion; Gait optimization

Indexed keywords

ARTIFICIAL INTELLIGENCE;

BAYESIAN OPTIMIZATION; BIPEDAL LOCOMOTION; EXPERT KNOWLEDGE; GAIT-OPTIMIZATION; PERFORMANCE CRITERION; PERFORMANCE METRICES; PROOF OF CONCEPT; STOCHASTIC OPTIMAL CONTROL;

ENERGY EFFICIENCY;

EID: 84905816218     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-09584-4_25     Document Type: Conference Paper

References (29)

1. Auer, P.: Using confidence bounds for exploitation-exploration trade-offs. J. Mach. Learn. Res. (JMLR) 3, 397-422 (2003)
2. Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. (JMLR) 13, 281-305 (2012)
3. Bertsekas, D.P.: Dynamic Programming and Optimal Control, 3rd edn. Athena Scientific, Belmont (2007)
4. Brochu, E., Cora, V.M., De Freitas, N.:. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprint arXiv:1012.2599 (2010)
5. Byrd, R.H., Lu, P., Nocedal, J., Zhu, C.: A limited memory algorithm for bound constrained optimization. SIAM J. Sci. Comput. 16(5), 1190-1208 (1995)
6. Calandra, R., Seyfarth, A., Peters, J., Deisenroth, M.P.: An experimental comparison of Bayesian optimization for bipedal locomotion. In: Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 1951-1958 (2014)
7. Chernova, S., Veloso, M.: An evolutionary approach to gait learning for four-legged robots. In: Proceedings of Intelligent Robots and Systems (IROS), vol. 3, pp. 2562-2567. IEEE (2004)
8. Cox, D.D., John, S.: SDO: a statistical method for global optimization. In: Alexandrov, N., Hussaini, M.Y. (eds.) Multidisciplinary Design Optimization: State of the Art, pp. 315-329. SIAM, Philadelpha (1997)
9. Garnett, R., Osborne, M.A., Roberts, S.J.: Bayesian optimization for sensor set selection. In: Information Processing in Sensor Networks, pp. 209-219. ACM (2010)
10. Gopalan, N., Deisenroth, M.P., Peters, J.: Feedback error learning for rhythmic motor primitives. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (2013)
11. Grizzle, J.W., Abba, G., Plestan, F.: Asymptotically stable walking for biped robots: analysis via systems with impulse effects. IEEE Trans. Autom. Control 46(1), 51-64 (2001)
12. Hennig, P., Schuler, C.J.: Entropy search for information-efficient global optimization. J. Mach. Learn. Res. 13, 1809-1837 (2012)
13. Ijspeert, A.J., Nakanishi, J., Schaal, S.: Learning attractor landscapes for learning motor primitives. In: Becker, S., Thrun, S., Obermayer, K. (eds.) Advances in Neural Information Processing Systems (NIPS), vol. 15. MIT Press, Cambridge (2003)
14. Jones, D.R.: A taxonomy of global optimization methods based on response surfaces. J. Global Optim. 21(4), 345-383 (2001)
15. Jones, D.R., Perttune, C.D., Stuckman, B.E.: Lipschitzian optimization without the Lipschitz constant. J. Optim. Theor. Appl. 79(1), 157-181 (1993)
16. Kober, J., Peters, J.: Learning motor primitives for robotics. In: International Conference on Robotics and Automation (ICRA) (2009)
17. Kushner, H.J.: A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise. J. Basic Eng. 86, 97 (1964)
18. Lizotte, D., Wang, T., Bowling, M., Schuurmans, D.: Automatic gait optimization with Gaussian process regression. In: Proceedings of International Joint Conferences on Artificial Intelligence (IJCAI), pp. 944-949 (2007)
19. Mockus, J., Tiesis, V., Zilinskas, A.: The application of Bayesian methods for seeking the extremum. Towards Global Optim. 2, 117-129 (1978)
20. Nakanishi, J., Morimoto, J., Endo, G., Cheng, G., Schaal, S., Kawato, M.: Learning from demonstration and adaptation of biped locomotion. Robot. Autonom. Syst. 47(2), 79-91 (2004)
21. Osborne, M.A., Garnett, R., Roberts, S.J.: Gaussian processes for global optimization. In: 3rd International Conference on Learning and Intelligent Optimization (LION3), pp. 1-15 (2009)
22. Pongas, D., Billard, A., Schaal, S.:. Rapid synchronization and accurate phase-locking of rhythmic motor primitives. In: Proceedings of Intelligent Robots and Systems (IROS), pp. 2911-2916. IEEE (2005)
23. Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. The MIT Press, Cambridge (2006)
24. Renjewski, D., Seyfarth, A.: Robots in human biomechanics - a study on ankle push-off in walking. Bioinspir. Biomimetics 7(3), 036005 (2012)
25. Renjewski, D.: An engineering contribution to human gait biomechanics. Ph.D. Thesis, TU Ilmenau (2012)
26. Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Advances in Neural Information Processing Systems (NIPS) (2012)
27. Srinivas, N., Krause, A., Kakade, S., Seeger, M.: Gaussian process optimization in the bandit setting: no regret and experimental design. In: Proceedings of International Conference on Machine Learning (ICML), pp. 1015-1022 (2010)
28. Tedrake, R., Weirui Zhang, T., Sebastian Seung, H.: Learning to walk in 20 minutes. In: Proceedings of the 14th Yale Workshop on Adaptive and Learning Systems (2005)
29. Tesch, M., Schneider, J., Choset, H.: Using response surfaces and expected improvement to optimize snake robot gait parameters. In: Proceedings of Intelligent Robots and Systems (IROS), pp. 1069-1074. IEEE (2011)