Frontiers in Computational Neuroscience (Oct 2013)
Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems
Abstract
A salient feature of human motor skill learning is the ability to exploitsimilarities across related tasks.In biological motor control, it has been hypothesized that muscle synergies,coherent activations of groups of muscles, allow for exploiting shared knowledge.Recent studies have shown that a rich set of complex motor skills can be generated bya combination of a small number of muscle synergies.In robotics, dynamic movement primitives are commonlyused for motor skill learning. This machine learning approach implements a stable attractor systemthat facilitates learning and it can be used in high-dimensional continuous spaces. However, it does not allow for reusing shared knowledge, i.e. for each task an individual set of parameters has to be learned.We propose a novel movement primitive representationthat employs parametrized basis functions, which combines the benefits of muscle synergiesand dynamic movement primitives. For each task asuperposition of synergies modulates a stable attractor system.This approach leads to a compact representation of multiple motor skills andat the same time enables efficient learning in high-dimensional continuous systems.The movement representation supports discrete and rhythmic movements andin particular includes the dynamic movement primitive approach as a special case.We demonstrate the feasibility of the movement representation in three multi-task learning simulated scenarios.First, the characteristics of the proposed representation are illustrated in a point-mass task.Second, in complex humanoid walking experiments,multiple walking patterns with different step heights are learned robustly and efficiently.Finally, in a multi-directional reaching task simulated with a musculoskeletal modelof the human arm, we show how the proposed movement primitives can be used tolearn appropriate muscle excitation patterns and to generalize effectively to new reaching skills.
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