Simplified and effective motor control based on muscle synergies to exploit musculoskeletal dynamics
By Max Berniker, A. Jarc, Emilio Bizzi, Matthew C. Tresch. 2009
193 citations recorded by the source corpus.
The basic hypothesis of producing a range of behaviors using a small set of motor commands has been proposed in various forms to explain motor behaviors ranging from basic reflexes to complex voluntary movements. Yet many fundamental questions regarding this long-standing hypothesis remain unanswered. Indeed, given the prominent nonlinearities and high dimensionality inherent in the control of biological limbs, the basic feasibility of a low-dimensional controller and an underlying principle for its creation has remained elusive. We propose a principle for the design of such a controller, that it endeavors to control the natural dynamics of the limb, taking into account the nature of the task being performed. Using this principle, we obtained a low-dimensional model of the hindlimb and a set of muscle synergies to command it. We demonstrate that this set of synergies was capable of producing effective control, establishing the viability of this muscle synergy hypothesis. Finally, by combining the low-dimensional model and the muscle synergies we were able to build a relatively simple controller whose overall performance was close to that of the system's full-dimensional nonlinear controller. Taken together, the results of this study establish that a low-dimensional controller is capable of simplifying control without degrading performance.
Subfields: Biomedical Engineering, Cognitive Neuroscience
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
In Cognitive Neuroscience
- Darwin G. Caldwell
- V. Cheung
- Petar Kormushev
- Sylvain Calinon
- Emilio Bizzi
- Jan Peters
- Gerhard Neumann
- Sonia Chernova
Citations in the corpus
- Active learning of inverse models with intrinsically motivated goal exploration in robots (2013, neutral): “…(lower time scale) considers the goal-directed active choice and active exploration of lower-level actions to be taken to reach the goals selected at the higher level, and depending on local measures of interest related to the evolution of the quality of learned inverse and/or forward models;”
- The neural origin of muscle synergies (2013, background): “Thus, our argument that muscle synergies could have a neural origin is not incompatible with the idea that the precise structuring of each muscle synergy incorporates knowledge of both the musculo-skeletal dynamics (Berniker et al., 2009) and other biomechanical properties of the limb.”
- Robot motor skill coordination with EM-based Reinforcement Learning (2010, background): “Pardo et al proposed in [14], [15] a framework to learn coordination for simple rest-to-rest movements, by taking inspiration of the motor coordination, joint synergies, and the importance of coupling in motor control [16]–[19].”