Constructive incremental learning from only local information
Neural Computation
Optimal trajectory formation of constrained human arm reaching movements
Biological Cybernetics
A unifying framework for robot control with redundant DOFs
Autonomous Robots
Learning to Control in Operational Space
International Journal of Robotics Research
A novel method for learning policies from constrained motion
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
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Many everyday human skills can be framed in terms of performing some task subject to constraints imposed by the environment. Constraints are usually unobservable and frequently change between contexts. In this paper, we present a novel approach for learning (unconstrained) control policies from movement data, where observations are recorded under different constraint settings. Our approach seamlessly integrates unconstrained and constrained observations by performing hybrid optimisation of two risk functionals. The first is a novel risk functional that makes a meaningful comparison between the estimated policy and constrained observations. The second is the standard risk, used to reduce the expected error under impoverished sets of constraints. We demonstrate our approach on systems of varying complexity, and illustrate its utility for transfer learning of a car washing task from human motion capture data.