Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
Statistical Learning for Humanoid Robots
Autonomous Robots
Efficient greedy learning of Gaussian mixture models
Neural Computation
Incremental Online Learning in High Dimensions
Neural Computation
MOSAIC Model for Sensorimotor Learning and Control
Neural Computation
On-line regression algorithms for learning mechanical models of robots: A survey
Robotics and Autonomous Systems
Dynamics model abstraction scheme using radial basis functions
Journal of Control Science and Engineering - Special issue on Dynamic Neural Networks for Model-Free Control and Identification
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For stationary systems, efficient techniques for adaptive motor control exist which learn the system’s inverse dynamics online and use this single model for control. However, in realistic domains the system dynamics often change depending on an external unobserved context, for instance the work load of the system or contact conditions with other objects. A solution to context-dependent control is to learn multiple inverse models for different contexts and to infer the current context by analyzing the experienced dynamics. Previous multiple model approaches have only been tested on linear systems. This paper presents an efficient multiple model approach for non-linear dynamics, which can bootstrap context separation from context-unlabeled data and realizes simultaneous online context estimation, control, and training of multiple inverse models. The approach formulates a consistent probabilistic model used to infer the unobserved context and uses Locally Weighted Projection Regression as an efficient online regressor which provides local confidence bounds estimates used for inference.