Learning CPG sensory feedback with policy gradient for biped locomotion for a full-body humanoid

Proceedings of the National Conference on Artificial Intelligence

Volumn 3, Issue , 2005, Pages 1267-1273

  Endo, Gen ^a,b   Morimoto, Jun ^b,c   Matsubara, Takamitsu ^b,d   Nakanishi, Jun ^b,c   Cheng, Gordon ^b,c  

^a SONY CORPORATION   (Japan)

^b ADVANCED TELECOMMUNICATIONS RESEARCH INSTITUTE INTERNATIONAL   (Japan)

^c JAPAN SCIENCE AND TECHNOLOGY AGENCY   (Japan)

^d NARA INSTITUTE OF SCIENCE AND TECHNOLOGY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER HARDWARE; COMPUTER SCIENCE; COMPUTER SIMULATION; INFORMATION TECHNOLOGY; LEARNING ALGORITHMS; STATE SPACE METHODS; THREE DIMENSIONAL;

DIMENSIONALITY; FEED-BACK CONTROLLERS; FULL-BODY HUMANOIDS; POLICY GRADIENT METHOD;

BIPED LOCOMOTION;

EID: 29344443108     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. Benbrahim, H., and Franklin, J. A. 1997. Biped dynamic walking using reinforcement learning. Robotics and Autonomous Systems 22:283-302.
2. Cohen, A. H. 2003. Control principle for locomotion -looking toward biology. In 2nd International Symposium on Adaptive Motion of Animals and Machines. (CD-ROM, TuP-K-1).
3. Doya, K. 2000. Reinforcement learning in continuous time and space. Neural Computation 12:219-245.
4. Endo, G.; Nakanishi, J.; Morimoto, J.; and Cheng, G. 2005. Experimental studies of a neural oscillator for biped locomotion with QRIO. In IEEE International Conference on Robotics and Automation, 598-604.
5. Hase, K., and Yamazaki, N. 1998. Computer simulation of the ontogeny of biped walking. Anthropological Science 106(4):327-347.
6. Ishida, T., and Kuroki, Y. 2004. Development of sensor system of a small biped entertainment robot. In IEEE International Conference on Robotics and Automation, 648-653.
7. Ishida, T.; Kuroki, Y; and Yamaguchi, J. 2003. Mechanical system of a small biped entertainment robot. In IEEE International Conference on Intelligent Robots and Systems, 1129-1134.
8. Jaakkola, T.; Singh, S. P.; and Jordan, M. I. 1995. Re-inforcement learning algorithm for partially observable Markov decision problems. 7:345-352.
9. Kaelbling, L. P.; Littman, M. L.; and Cassandra, A. R. 1998. Planning and acting in partially observable stochastic domains. Artificial Intelligence 101:99-134.
10. Kimura, H., and Kobayashi, S. 1998. An analysis of actor/critic algorithms using eligibility traces: Reinforcement learning with imperfect value function. In International Conference on Machine Learning, 278-286.
11. Kimura, H.; Fukuoka, Y.; and Cohen, A. H. 2003. Biologically inspired adaptive dynamic walking of a quadruped robot. In 8th International Conference on the Simulation of Adaptive Behavior, 201-210.
12. Konda, V., and Tsitsiklis, J. 2003. On actor-critic algorithms. Society for Industrial and Applied Mathematics 42(4):1143-1166.
13. Matsubara, T.; Morimoto, J.; Nakanishi, J.; and Doya, K. 2005. Learning feedback pathways in CPG with policy gradient for biped locomotion. In IEEE International Conference on Robotics and Automation, 4175-4180.
14. Matsuoka, K. 1985. Sustained oscillations generated by mutually inhibiting neurons with adaptation. Biological Cybernetics 52:345-353.
15. Mori, T.; Nakamura, Y.; Sato, M.; and Ishii, S. 2004. Reinforcement learning for a cpg-driven biped robot. In Nineteenth National Conference on Artificial Intelligence, 623-630.
16. Peters, J.; Vijayakumar, S.; and Schaal, S. 2003. Reinforcement learning for humanoid robotics. In International Conference on Humanoid Robots. (CD-ROM).
17. Sutton, R. S.; McAllester, D.; Singh, S.; and Mansour, Y. 2000. Policy gradient methods for reinforcement learning with imperfect value function. Advances in Neural Information Processing Systems 12:1057-1063.
18. Taga, G. 1995. A model of the neuro-musculo-skeletal system for human locomotion I. emergence of basic gait. Biological Cybernetics 73:97-111.
19. Tedrake, R.; Zhang, T. W.; and Seung, H. S. 2004. Stochastic policy gradient reinforcement learning on a simple 3D biped. In International Conference on Intelligent Robots and Systems, 2849-2854.
20. Williamson, M. 1998. Neural control of rhythmic arm movements. Neural Networks 11(7-8): 1379-1394.