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Extracting motor synergies from random movements for low-dimensional task-space control of musculoskeletal robots.
This paper proposes to extract motor synergies from a subset of randomly generated reaching-like movement data, and presents a kernel-based regression formulation to estimate the forward and the inverse dynamics, and a sliding controller in order to cope with estimation error. Expand