Inequality constraints in the process of jumping
Applied Mathematics and Computation - Special issue on dynamics and control
Advanced Robotics: Redundancy and Optimization
Advanced Robotics: Redundancy and Optimization
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A Mathematical Introduction to Robotic Manipulation
A Mathematical Introduction to Robotic Manipulation
Learning an Agent's Utility Function by Observing Behavior
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Robot Learning From Demonstration
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
On clusterings: Good, bad and spectral
Journal of the ACM (JACM)
Optimal trajectory formation of constrained human arm reaching movements
Biological Cybernetics
Incremental Online Learning in High Dimensions
Neural Computation
Learning Nonlinear Image Manifolds by Global Alignment of Local Linear Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A unifying framework for robot control with redundant DOFs
Autonomous Robots
Learning to Control in Operational Space
International Journal of Robotics Research
Utility elicitation as a classification problem
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Modified Newton's method applied to potential field-based navigation for mobile robots
IEEE Transactions on Robotics
Correspondence Mapping Induced State and Action Metrics for Robotic Imitation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the implementation of velocity control for kinematicallyredundant manipulators
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Movement generation that is consistent with observed or demonstrated behaviour is an efficient way to seed movement planning in complex, high-dimensional movement systems like humanoid robots. We present a method for learning potential-based policies from constrained motion data. In contrast to previous approaches to direct policy learning, our method can combine observations from a variety of contexts where different constraints are in force, to learn the underlying unconstrained policy in form of its potential function. This allows us to generalise and predict behaviour where novel constraints apply. We demonstrate our approach on systems of varying complexity, including kinematic data from the ASIMO humanoid robot with 22 degrees of freedom.