S-learning: A model-free, case-based algorithm for robot learning and control

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

Volumn 5650 LNAI, Issue , 2009, Pages 285-297

  Rohrer, Brandon ^a  

^a SANDIA NATIONAL LABORATORIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTUATORS AND SENSORS; CALIBRATION INFORMATION; CASE BASED LEARNING; CONTROL ALGORITHMS; ENVIRONMENTAL STRUCTURE; MODEL FREE; OPTIMAL PATHS;

EDUCATION; LEARNING ALGORITHMS; LIGHT; LIGHT MEASUREMENT; LIGHT SOURCES; ROBOT PROGRAMMING; ROBOTS;

CASE BASED REASONING;

EID: 70350349521     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02998-1_21     Document Type: Conference Paper

References (13)

1. Albus, J.: A new approach to manipulator control: Cerebellar model articulation controller (CMAC). Journal of Dynamic Systems, Measurement and Control 97, 220-227 (1975)
2. Hulet, S., Rohrer, B., Warnick, S.: A study in pattern assimilation for adaptation and control. In: 8th Joint Conference on Information Systems (2005)
3. Kavraki, L.E., Svestka, P., Latombe, J.-C., Overmars, M.H.: Probabalistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation 12(4), 566-580 (1996)
4. Levinson, S.E.: Mathematical Models for Speech Technology, pp. 238-239. John Wiley and Sons, Chichester (2005)
5. McKinstry, J.L., Edelman, G.M., Krichmar, J.L.: A cerebellar model for predicitive motor control tested in a brain-based device. Proceedings of the National Academy of Sciences 103(9), 3387-3392 (2006)
6. Moore, A.W., Atkeson, C.G.: The parti-game algorithm for variable resolution reinforcement learning in multidimensional state-spaces. Machine Learning 21, 199-233 (1995)
7. Morimoto, J., Zeglin, G., Atkeson, C.G.: Minimax differential dynamic programming: Application to a biped walking robot. In: Proceedings of the IEEE/RSJ Intl. Conference on Intelligent Robots and Systems, pp. 1927-1932 (2003)
8. Rohrer, B.: S-learning: A biomimetic algorithm for learning, memory, and control in robots. In: Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering (2007)
9. Rohrer, B.: Robust performance of autonomous robots in unstructured environments. In: Proceedings of the American Nuclear Society 2nd International Joint Topical Meeting on Emergency Preparedness and Response and Robotics and Remote Systems (2008)
10. Sutton, R.S.: Planning by incremental dynamic programming. In: Proceedings of the Eighth International Workshop on Machine Learning, pp. 353-357. Morgan Kaufmann, San Francisco (1991)
11. Tarraf, D.C., Megretski, A., Dahleh, M.A.: A framework for robust stability of systems over finite alphabets. IEEE Transactions on Automatic Control (June 2008); To appear as a regular paper in the IEEE Transactions on Automatic Control (scheduled for June 2008)
12. Tassa, Y., Erez, T., Smart, B.: Receding horizon differential dynamic programming. In: Advances in Neural Information Processing Systems, pp. 1465-1472. MIT Press, Cambridge (2008)
13. Watkins, C.J.C.H.: Learning from Delayed Rewards. PhD thesis, Cambridge University, Cambridge, England (1989)