Synthesis and Adaptation of Effective Motor Synergies for the Solution of Reaching Tasks
This work addresses control optimization for robotic or biomechanical agents, but it is incremental as it builds on existing synergy hypotheses without broad breakthroughs.
The authors tackled the problem of generating open-loop controllers for point-to-point reaching tasks by proposing a method based on muscle synergies, which reduces control dimensionality while maintaining performance, as evaluated in a planar kinematic chain with quantified results.
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.