Collaborative control in a humanoid dynamic task

ICINCO 2007 - 4th International Conference on Informatics in Control, Automation and Robotics, Proceedings

Volumn 1 RA, Issue , 2007, Pages 174-180

  Pardo, Diego ^a   Angulo, Cecilio ^a  

^a UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

Author keywords

Coordination policy; Reinforcement learning; Robot control architecture; Sensorimotor learning

Indexed keywords

ARTICULATED MOBILE ROBOT; COLLABORATIVE CONTROL; CONTROL FUNCTIONS; COORDINATION POLICY; DEGREES OF FREEDOM; DYNAMIC BEHAVIORS; HUMANOID DYNAMICS; MODULARIZATION; REINFORCEMENT LEARNING TECHNIQUES; ROBOT CONTROL ARCHITECTURE; SENSORIMOTOR LEARNING; SPECIFIC TASKS; STANDARD PROCEDURES;

COORDINATION REACTIONS; EDUCATION; FORCE CONTROL; INTELLIGENT ROBOTS; LEARNING ALGORITHMS; MODULAR CONSTRUCTION; REINFORCEMENT; ROBOTICS;

REINFORCEMENT LEARNING;

EID: 67649968099     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Daley, S. and Liu, G. (1999). Optimal pid tuning using direct search algorithms. Computing and Control Engineering Journal, l0(2):25 1-56.
2. Fujita, M. and Kitano, H. (1998). Development of an autonomous quadruped robot for robot entertainment. Autonomous Robots, 5(1):7-l8.
3. Hirai, K., Hirose, M., Haikawa, Y., and Takenaka, T. (1998). The development of honda humanoid robot. In Proceedings of the IEEE International Conference on Robotics and Automation, ICRA.
4. Jabri, M. and Flower, B. (1992). Weight perturbation: An optimal architecture and learning technique for analog VLSI feedforward and recurrent multilayer networks. IEEE Transactions on Neural Networks, 3(1): 154-157.
5. Kaneko, K., Kanehiro, F., Kajita, S., Hirukawa, H., Kawasald, T., Hirata, M., Akachi, K., and Isozumi, T. (2004). Humanoid robot hrp-2. In Proceedings of the IEEE International Conference on Robotics and Automation, ICRA.
6. Koszalka, L., Rudek, R., and Pozniak-Koszalka, I. (2006). An idea for using reinforcement learning in adaptive control systems. In Proceedings of the IEEE mt Conference on Networking, Systems and Mobile Communications and Learning Technologies, Kerkrade, Netherlands.
7. Kuroki, Y., Blank, B., Mikami, T., Mayeux, P., Miyamoto, A., Playter, R., Nagasaya, K., Raibert, M., Nagano, M., and Yamaguchi, J. (2003). A motion creating system for a small biped entretainment robot In Proceedings of the IEEE International Conference on Intelligent Robots and Systems, IROS.
8. Michel, 0. (2004). Webots: Professional mobile robot simulation. Journal of Advanced Robotics Systems, 1 (l):39-42.
9. Rosenstein, M. T. (2003). Learning to exploit dynamics for robot motor coordination. PhD thesis, University of Massachusetts, Amherst.
10. Rosenstein, M. T. and Barto, A. G. (2001). Robot weightlifting by direct policy search. In Proceedings of the IEEE International Conference on Artificial Intelligence, IJCAI, pages 839-846.
11. Sutton, R., McAllester, D., Singh, S., and Mansour, Y. (2000). Policy gradient methods for reinforcement learning with function approximation. Advances in Neural Information Processing Systems, 12:1057- 1063.
12. Vukobratovic, M. and Stepanenko, J. (1972). On the stability of anthropomorphic systems. Mathematical Biosciences, 15:1-37.
13. Webots. http://www.cyberbotics.com. Commercial Mobile Robot Simulation Software.
14. Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8:229-256.