Foundations of cognitive science
Learning to Combine Motor Primitives Via Greedy Additive Regression
The Journal of Machine Learning Research
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Analysis of motor synergies utilization for optimal movement generation for a human-like robotic arm
International Journal of Automation and Computing
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Studies of the motor synergies reveal several interesting findings. First, optimal motor actions can be generated by summing a small number of scaled and time-shifted motor synergies, indicating that optimal movements can be planned in a low-dimensional space by using optimal motor synergies as motor primitives or building blocks. Second, some optimal synergies are task independent—they arise regardless of the task context—whereas other synergies are task dependent—they arise in the context of one task but not in the contexts of other tasks. Biological organisms use a combination of task-independent and task-dependent synergies. Our work suggests that this may be an efficient combination for generating optimal motor actions from motor primitives. Third, optimal motor actions can be rapidly acquired by learning new linear combinations of optimal motor synergies. This result provides further evidence that optimal motor synergies are useful motor primitives. Fourth, synergies with similar properties arise regardless if one uses an arm controlled by torques applied at the joints or an arm controlled by muscles, suggesting that synergies, when considered in “movement space,” are more a reflection of task goals and constraints than of fine details of the underlying hardware.