Chris Atkeson: Selected Publications

Local Learning

An overview of work on local learning algorithms is given by:
Atkeson, C. G., Moore, A. W., & Schaal, S.
"Locally Weighted Learning." Artificial Intelligence Review, 11:11-73, 1997.
gzipped postscript

An overview of local learning applied to robots is given by:
Atkeson, C. G., Moore, A. W., & Schaal, S.
"Locally Weighted Learning for Control." Artificial Intelligence Review, 11:75-113, 1997.
gzipped postscript

A mixture of experts approach to local learning is presented in:
Schaal, S., & Atkeson, C. G.
"From Isolation to Cooperation: An Alternative View of a System of Experts" In: D.S. Touretzky, and M.E. Hasselmo (Eds.), Advances in Neural Information Processing Systems 8. Cambridge, MA: MIT Press. 1996.

Looking at local learning from a neural network point of view:
Atkeson, C. G., and S. Schaal,
``Memory-Based Neural Networks For Robot Learning'', Neurocomputing, 9(3):243-69, 1995.
gzipped postscript

Learning From Demonstration

How can a robot learn from watching a human?
Atkeson, C. G. and S. Schaal
``Robot Learning From Demonstration'', Machine Learning: Proceedings of the Fourteenth International Conference (ICML '97), Edited by Douglas H. Fisher, Jr. pp. 12-20, Morgan Kaufmann, San Francisco, CA, 1997. gzipped postscript

Reinforcement Learning

An approach to using local planning to speed up global learning is given in:
Atkeson, C. G.
"Using Local Trajectory Optimizers To Speed Up Global Optimization In Dynamic Programming", In: Neural Information Processing Systems 6. Morgan Kaufmann, 1994. gzipped postscript

Is model-based or non-model-based reinforcement learning more efficient?
Atkeson, C. G. and J. C. Santamaria,
``A Comparison of Direct and Model-Based Reinforcement Learning'', International Conference on Robotics and Automation, 1997.
gzipped postscript

A variable resolution approach to reinforcement learning is presented in:
Moore, A. W., and C. G. Atkeson,
"The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State Spaces" Machine Learning, 21(3):199-233, 1995.
gzipped postscript

Solving discrete state reinforcement learning policies efficiently:
Moore, A. W., and C. G. Atkeson,
"Memory-based Reinforcement Learning: Converging with Less Data and Less Real Time" Machine Learning, 13:103-130, 1993.
gzipped postscript

Robot Learning

Applying local learning to robot learning:
Schaal, S., and C. G. Atkeson,
``Robot Juggling: An Implementation of Memory-based Learning'', Control Systems Magazine, 14(1):57-71, 1994.

A book describing parametric model-based learning for robots:
C. H. An, C. G. Atkeson, and J. M. Hollerbach,
Model-Based Control of a Robot Manipulator, MIT Press, Cambridge, Massachusetts, 1988.

Robot Strategies

Schaal, S., D. Sternad and C. G. Atkeson,
``One-handed Juggling: Dynamical Approaches to a Rhythmic Movement Task'', Journal of Motor Behavior, 28(2):165-183, 1996.

Schaal, S. and C. G. Atkeson,
``Open Loop Stable Control Strategies for Robot Juggling'', In: IEEE International Conference on Robotics and Automation, Vol.3, pp.913-918, Atlanta, Georgia, 1993.

Back to Chris Atkeson's home page
cga@cc.gatech.edu