Forward models for physiological motor control
Neural Networks - 1996 Special issue: four major hypotheses in neuroscience
Artificial Intelligence Review - Special issue on lazy learning
Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
Scalable Techniques from Nonparametric Statistics for Real Time Robot Learning
Applied Intelligence
Multiple model-based reinforcement learning
Neural Computation
Feedback error learning and nonlinear adaptive control
Neural Networks
Incremental Online Learning in High Dimensions
Neural Computation
MOSAIC Model for Sensorimotor Learning and Control
Neural Computation
Constructive Incremental Learning from Only Local Information
Neural Computation
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Humans have the ability to learn novel motor tasks while manipulating the environment. Several models of motor learning have been proposed in the literature, but few of them address the problem of retention and interference of motor memory. The modular selection and identification for control (MOSAIC) model, originally proposed by Wolpert and Kawato, is one of the most relevant contributions; it suggests a possible strategy on how the human motor control system learns and adapts to novel environments. MOSAIC employs the concept of forward and inverse models. The same group later proposed the hidden Markov model (HMM) MOSAIC, which affords learning multiple tasks. The significant drawback of this second approach is that the HMM must be trained with a complete data set that includes all contexts. Since the number of contexts or modules is fixed from the onset, this approach does not afford incremental learning of new tasks. In this letter, we present an alternative architecture to overcome this problem, based on a nonparametric regression algorithm, named locally weighted projection regression (LWPR). This network structure develops according to the contexts allowing incremental training. Of notice, interaction force is used to disambiguate among different contexts. We demonstrate the capability of this alternative architecture with a simulated 2 degree-of-freedom representation of the human arm that learns to interact with three distinct objects, reproducing the same test paradigm of the HMM MOSAIC. After learning the dynamics of the three objects, the LWPR network successfully learns to compensate for a novel velocity-dependent force field. Equally important, it retains previously acquired knowledge on the interaction with the three objects. Thus, this architecture allows both incremental learning of new tasks and retention of previously acquired knowledge, a feature of human motor learning and memory.