LEARNING to ACT from OBSERVATION and PRACTICE

International Journal of Humanoid Robotics

Volumn 1, Issue 4, 2004, Pages 585-611

  Bentivegna, Darrin C ^a,b   Atkeson, Christopher G ^a,c   Ude, Aleš ^a,d   Cheng, Gordon ^a,b  

^a ADVANCED TELECOMMUNICATIONS RESEARCH INSTITUTE INTERNATIONAL   (Japan)

^b JAPAN SCIENCE AND TECHNOLOGY AGENCY   (Japan)

^c CARNEGIE MELLON UNIVERSITY   (United States)

^d JO EF STEFAN INSTITUTE   (Slovenia)

Author keywords

Air hockey; Humanoid robot; Imitation; Learning from observation; Locally weighted learning; Movement primitives

Indexed keywords

SPORTS;

AIR HOCKEYS; CONDITION; DYNAMIC TASKS; HUMANOID ROBOT; IMITATION; LEARN+; LEARNING FROM OBSERVATION; LEARNING PROBLEM; LOCALLY WEIGHTED LEARNING; MOVEMENT PRIMITIVES;

ANTHROPOMORPHIC ROBOTS;

EID: 34250645111     PISSN: 02198436     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0219843604000307     Document Type: Article

References (18)

1. C. G. Atkeson, J. G. Hale, F. Pollick, M. Riley, S. Kotosaka, S. Schaal, T. Shibata, G. Tevatia, A. Ude, S. Vijaykumar and M. Kawato, Using humanoid robots to study human behavior, IEEE Intell. Syst. 15(4), 46-55 (2000).
2. C. G. Atkeson, A. W. Moore and S. Schaal, Locally weighted learning, Artif. Intell. Rev. 11, 11-73 (1997).
3. D. C. Bentivegna and C. G. Atkeson, Learning from observation using primitives, in Proc. IEEE Int. Conf. Robotics and Automation, Seoul, Korea, 2001.
4. D. C. Bentivegna and C. G. Atkeson, A framework for learning from observation using primitives, in Proc. RoboCup 2002 Int. Symp. Fukuoka, Japan, 2002.
5. D. C. Bentivegna, A. Ude, C. G. Atkeson, and G. Cheng, Humanoid robot learning and game playing using PC-based vision, in Proc. 2002 IEEE/RSJ Int. Conf. Intelligent Robots and Systems, Switzerland, 2002.
6. B. Bishop, P. Shirkey and M. Spong, An experimental testbed for intelligent control, American Control Conf., Seattle, 1995.
7. A. Fod, M. Mataric and O. Jenkins, Automated derivation of primitives for movement classification, in 1st IEEE-RAS Int. Conf. Humanoid Robotics (Humanoids-2000) (MIT, Cambridge, MA, 2000).
8. W. Iba, Learning to classify observed motor behavior, in Proc. Int. Joint Conf. Artificial Intelligence, 1991, pp. 732-738.
9. G. Kaminka, M. Fidanboylu, A. Chang and M. Veloso, Learning the sequential coordinated behavior of teams from observation, in Proc. 2002 Int. RoboCup Symp., 2002.
10. A. McGovern and A. G. Barto, Automatic discovery of subgoals in reinforcement learning using diverse density, in Proc. 18th Int. Conf. Machine Learning, 2001.
11. J. Park, C. B. Partridge and M. W. Spong, Neural network based state prediction for strategy planning of an air hockey robot, J. Robot. Syst. 18(4), 187-196 (2001).
12. C. B. Partridge and M. W. Spong, Control of planar rigid body sliding with impacts and friction, in Int. J. Robot. Res. 19(4), 336-348 (2000).
13. S. Schaal, C. Atkeson and S. Vijayakumar, Scalable locally weighted statistical techniques for real time robot learning, Appl. Intell. (Special issue on Scalable Robotic Applications of Neural Networks) 17(1), 49-60 (2002).
14. A. Ude and C. G. Atkeson, Real-time visual system for interaction with a humanoid robot, in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, Maui, Hawaii, October/November 2001, pp. 746-751.
15. S. Vijayakumar, A. D'Souza, T. Shibata, J. Conradt and S. Schaal, Statistical learning for humanoid robots, Auton. Robot. 12(1), 55-69 (2002).
16. S. Vijayakumar and S. Schaal, Locally weighted projection regression: An O(n) algorithm for incremental real time learning in high dimensional spaces, in Proc. 17th Int. Conf. Machine Learning (ICML 2000), Stanford, CA, 2000.
17. D. M. Wolpert and M. Kawato, Multiple paired forward and inverse models for motor control, Neural Networks 11(7-8), 1317-1329 (1998).
18. Z. Zhang, A flexible new technique for camera calibration, Technical Report MSR-TR-98-71, Microsoft Research, Microsoft Corporation, Redmond, Washington, December 1998.