Advanced Robotics: Redundancy and Optimization
Advanced Robotics: Redundancy and Optimization
Modelling and Control of Robot Manipulators
Modelling and Control of Robot Manipulators
An inverse kinematics architecture enforcing an arbitrary number of strict priority levels
The Visual Computer: International Journal of Computer Graphics - Special section on implicit surfaces
Incremental Online Learning in High Dimensions
Neural Computation
A unifying framework for robot control with redundant DOFs
Autonomous Robots
A Library for Locally Weighted Projection Regression
The Journal of Machine Learning Research
Creating Brain-Like Intelligence
Creating Brain-Like Intelligence
New inference strategies for solving Markov decision processes using reversible jump MCMC
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
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In this chapter, we develop a new view on problems of movement control and planning from a Machine Learning perspective. In this view, decision making, control, and planning are all considered as an inference or (alternately) an information processing problem, i.e., a problem of computing a posterior distribution over unknown variables conditioned on the available information (targets, goals, constraints). Further, problems of adaptation and learning are formulated as statistical learning problems to model the dependencies between variables. This approach naturally extends to cases when information is missing, e.g., when the context or load needs to be inferred from interaction; or to the case of apprentice learning where, crucially, latent properties of the observed behavior are learnt rather than the motion copied directly. With this account, we hope to address the long-standing problem of designing adaptive control and planning systems that can flexibly be coupled to multiple sources of information (be they of purely sensory nature or higher-level modulations such as task and constraint information) and equally formulated on any level of abstraction (motor control variables or symbolic representations). Recent advances in Machine Learning provide a coherent framework for these problems.