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Learning to Combine Motor Primitives Via Greedy Additive Regression

Manu Chhabra, Robert A. Jacobs; 9(49):1535−1558, 2008.


The computational complexities arising in motor control can be ameliorated through the use of a library of motor synergies. We present a new model, referred to as the Greedy Additive Regression (GAR) model, for learning a library of torque sequences, and for learning the coefficients of a linear combination of sequences minimizing a cost function. From the perspective of numerical optimization, the GAR model is interesting because it creates a library of "local features"---each sequence in the library is a solution to a single training task---and learns to combine these sequences using a local optimization procedure, namely, additive regression. We speculate that learners with local representational primitives and local optimization procedures will show good performance on nonlinear tasks. The GAR model is also interesting from the perspective of motor control because it outperforms several competing models. Results using a simulated two-joint arm suggest that the GAR model consistently shows excellent performance in the sense that it rapidly learns to perform novel, complex motor tasks. Moreover, its library is overcomplete and sparse, meaning that only a small fraction of the stored torque sequences are used when learning a new movement. The library is also robust in the sense that, after an initial training period, nearly all novel movements can be learned as additive combinations of sequences in the library, and in the sense that it shows good generalization when an arm's dynamics are altered between training and test conditions, such as when a payload is added to the arm. Lastly, the GAR model works well regardless of whether motor tasks are specified in joint space or Cartesian space. We conclude that learning techniques using local primitives and optimization procedures are viable and potentially important methods for motor control and possibly other domains, and that these techniques deserve further examination by the artificial intelligence and cognitive science communities.

© JMLR 2008. (edit, beta)