Learning complex motions by sequencing simpler motion templates

Proceedings of the 26th International Conference On Machine Learning, ICML 2009

Volumn , Issue , 2009, Pages 753-760

  Neumann, Gerhard ^a   Maass, Wolfgang ^a   Peters, Jan ^b  

^a GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

^b MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX MOTION; CREDIT ASSIGNMENT PROBLEMS; HIGH QUALITY; MACHINE LEARNING METHODS; MOTOR SKILL LEARNING; MOTOR SKILLS; MOTOR TASKS; TEMPORAL ABSTRACTION; TIME-SCALES;

ABSTRACTING; ANIMALS; EDUCATION; MOTORS; REINFORCEMENT; ROBOT LEARNING;

LEARNING ALGORITHMS;

EID: 71149092646     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Arbib, M. A. (1981). Perceptual structures and distributed motor control. Handbook of physiology, section 2: The nervous system vol. ii, motor control, part 1, 1449-1480.
2. Atkeson, C., & Stephens, B. (2007). Multiple balance strategies from one optimization criterion. 7th IEEE-RAS International Conference on Humanoid Robots.
3. Atkeson, C. G., Moore, A. W., & Schaal, S. (1997). Locally weighted learning. Artificial Intelligence Review, 11, 11-73.
4. Bradtke, S. J., & Duff, M. O. (1995). Reinforcement learning methods for continuous-time markov decision problems. Advances in Neural Information Processing Systems 7, 7, 393-400.
5. Dautenhahn, K., & Nehaniv, C. L. (2002). Imitation in animals and artifacts. Campridge: MIT Press.
6. Ernst, D., Geurts, P., & Wehenkel, L. (2005). Tree-based batch mode reinforcement learning. J. Mach. Learn. Res., 6, 503-556.
7. Ghavamzadeh, M., & Mahadevan, S. (2003). Hierarchical policy gradient algorithms. Twentieth International Conference on Machine Learning (ICML-2003) (pp. 226-233).
8. Huber, M., & Grupen, R. A. (1998). Learning robot control - using control policies as abstract actions. In NIPS'98 Workshop: Abstraction and Hierarchy in Reinforcement Learning.
9. Ijspeert, A., Nakanishi, J., & Schaal, S. (2002). Learning attractor landscapes for learning motor primitives. Advances in Neural Information Processing Systems 15 (NIPS2002) (pp. 1523-1530).
10. Kober, J., & Peters, J. (2009). Policy search for motor primitives in robotics. Advances in Neural Information Processing Systems 22 (NIPS 2008) (pp. 849-856). MA: MIT Press.
11. Neumann, G., & Peters, J. (2009). Fitted Q-iteration by Advantage Weighted Regression. Advances in Neural Information Processing Systems 22 (NIPS 2008) (pp. 1177-1184). MA: MIT Press.
12. Riedmiller, M. (2005). Neural fitted Q-iteration - first experiences with a data efficient neural reinforcement learning method. Proceedings of the European Conference on Machine Learning (ECML) (pp. 317-328).
13. Sutton, R. S., Precup, D., & Singh, S. (1999). Between MDPs and Semi-MDPs: A framework for temporal abstraction in reinforcement learning. Artificial Intelligence, 112, 181-211.
14. Xu, X., & Antsaklis, P. (2002). An approach to optimal control of switched systems with internally forced switchings. Proceedings of the American Control Conference (pp. 148-153). Anchorage, USA.