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 exploit similarities 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 by a combination of a small number of muscle synergies. In robotics, dynamic movement primitives are commonly used for motor skill learning. This machine learning approach implements a stable attractor system that 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

SUBMITTER: Ruckert E 

PROVIDER: S-EPMC3797962 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature