Synthesis and Adaptation of Effective Motor Synergies for the Solution of Reaching Tasks

TL;DR

This paper introduces a method for generating open loop controllers using muscle synergy-inspired actuations, enabling autonomous synthesis and adaptation for reaching tasks while reducing control complexity.

Contribution

It presents a novel approach to synthesize and adapt motor synergies for control, improving efficiency and flexibility in reaching tasks.

Findings

Reduces control dimensionality significantly

Achieves effective reaching performance

Enables autonomous adaptation of synergies

Abstract

Taking inspiration from the hypothesis of muscle synergies, we propose a method to generate open loop controllers for an agent solving point-to-point reaching tasks. The controller output is defined as a linear combination of a small set of predefined actuations, termed synergies. The method can be interpreted from a developmental perspective, since it allows the agent to autonomously synthesize and adapt an effective set of synergies to new behavioral needs. This scheme greatly reduces the dimensionality of the control problem, while keeping a good performance level. The framework is evaluated in a planar kinematic chain, and the quality of the solutions is quantified in several scenarios.