Motor Synergies: A Concept in Motor Control


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Motor Synergies: A Concept in Motor Control

  1. 1. Motor Synergies: A Concept in Motor Control Marieke Rohde CCNR – Centre for Cognitive Neuroscience and Robotics Workshop on the Dynamical Systems approach to Life and Cognition University of Sussex, 8 and 9 March 2005
  2. 2. Structure 1. Motor Synergies 2. Directional Pointing • Linear Synergies in Human Directional Pointing • Evolutionary Robotics Example 3. Conclusions
  3. 3. 1.) Motor Synergies
  4. 4. Nicholas Bernstein • Nicolas Bernstein (1967, but really 1935) – Physiology of Activity, Biomechanics – Degrees of Freedom Problem
  5. 5. The Degrees of Freedom Problem • The “Cartesian Puppeteer” has to control a countless number of motor units. 7 26 2600 DoF
  6. 6. Motor Equivalence and Context- Conditioned Variability • Motor Equivalence – Redundancy through many degrees of freedom • Context-Conditioned Variability: – Anatomical (role of a muscle is context dependent) – Mechanical (command sent to muscles is ignorant against motion/nonmuscular forces) – Physiological (the spinal cord is not just a relay station)
  7. 7. The Solution • Systematic relationships between actuators (constraints) can reduce the degrees of freedom to form functional motor units (e.g. wheel position in a car)  Motor Synergies! • Skill Acquisition – First freezing degrees of freedom – Then freeing them and exploiting passive dynamics
  8. 8. Biological Evidence for Synergies • Systematicities in kinetics/kinematics: – Different types of gaits – Shooting – Breathing (Overview: Tuller et. Al. 1982) – Linear relation between shoulder and elbow torque (Gottlieb et. Al. 1999) • Complex behaviour as composition of synergies – Frog EMG data can be explained as linear combination of 7 linear synergies (Saltiel et. Al. 2001) Synergy between elbow and shoulder joint in a skilled marksperson
  9. 9. Problems with Motor Control through Synergy Control • The reminder of the homunculus – How does it work? • Acquisition and maintenance of synergies: – What is a good synergy? – What mechanism controls their development? • Combination of synergies: – Who deals with non-linearities? • Weiss, P. and M. Jeannerod (1998): – “Motor coordination is not the goal but a means to achieve the goal of an action”
  10. 10. If there’s no homunculus… …there’s no problem. Still, the observed phenomena require explanation.
  11. 11. Evolutionary Robotics • Things we can ask ourselves: – What does it imply if we have non-redundant models? – What does it imply if we do not have context-conditioned variability? • Things we can investigate: – More degrees of freedom – Anatomical, mechanical, physiological context dependence – Motor synergies in the absence of a homunculus – Impact of these factors on • Behaviour • Evolvability • The “phylogenetic learning” process
  12. 12. 2.) Directional Pointing
  13. 13. Linear Synergies in Human Directional Pointing • Gottlieb et. Al. 1997: – Directional Pointing in the sagittal plane – Linear relation: – Systematic variation of scaling constant with pointing direction – Linear synergies as an outcome of learning? • Zaal et. Al. 1999: – Linear Synergies are not learned, they constrain learning Direction against scaling constant Hand trajectories for pointing Pre-reaching period
  14. 14. Experiments (Work in Progress) • Simulated Robotic Arm • Proprioceptive (joint angle) + Directional Inputs • Fitness: Position at endpoint • Motor control: – “Garden CTRNNs” with two motor neurons per degree of freedom – “Split Brain CTRNNs” with separate controllers for joints – Linear Synergy networks with just one motor output and evolved scaling function (RBFNs) – 2 vs. 4 degrees of freedom for all of the above – 2 goals vs. up to 6 goals (additional goal once it has a certain level) • Most severe simplifications: – Hand of 4 degrees model is squashed between two planes – No gravity Screenshot of the simulated arm
  15. 15. Results • Performance – DoFs: 4 twice as good as 2 – Linear synergy are much better than CTRNNs (even linear linear synergies are comparable) – Split brains are not a lot worse than ordinary CTRNNs • How do they solve the problems? – 2 DoF’s: Frequently just use one joint – 4 DoF’s: exploit the invisible planes. – Linear Synergy: use different techniques, look a bit smoother – Split brains: Independence of joints very obvious
  16. 16. Results • Phylogeny – CTRNNs freeze degrees of freedom at first and then include them. – Networks use passive dynamics straight away. • Synergies – No linear synergies in any CTRNN controllers.
  17. 17. 3.) Conclusions
  18. 18. Conclusions: Evolutionary Robotics • Redundant degrees of freedom can facilitate evolving a controller, in spite of the much bigger search space and lead to a better solution • Learning under the constraint of linear synergy reshapes the search space and can lead to a very quick and successful evolution of different strategies (Careful with bias through model selection).
  19. 19. Conclusions: Synergies • A question we cannot answer (yet) is: Why are there linear synergies? • The acquisition of synergies: – Learning is not necessarily building up linear synergies. – The fact that the constraint of linear synergy boosts evolution suggests its suitability for developmental processes. – CTRNNs freeze and free DoFs. – Some kind of synergy gives CTRNNs an advantage over split brain CTRNNs. • The concept of synergy: – It is very useful to explain behaviour in abstract terms. – Particularly, if more complex behaviour is investigated. – Thinking in terms of synergies raises different questions – You just have to be clear about your relation to the Homunculus idea.
  20. 20. Future Research • Input models: – Make more CTRNN friendly – Visual inputs • Get rid of the invisible planes • Evolve constraints for lifetime development • Use synergies in a larger context (co-evolution of car and driver) • Investigate other forms of context conditioned variability
  21. 21. Any questions?
  22. 22. References • Arbib, M. A. (1981): Perceptual Structures and Distributed Motor Control. In: V. B. Brooks (ed.): Handbook of Physiology. Section 2: The Nervous System. Vol. II, Motor Control, Part 1. American Physiological Society, 1449-1480. • Bernstein, N. (1967): The Coordination and Regulation of Movements. Oxford: Pergamon. • Berthouze, L. and M. Lungarella (2004): Motor Skill Acquisition Under Environmental Perturbations: On the Necessity of Alternate Freezing and Freeing of Degrees of Freedom. Adaptive Behavior, 12(1). • Gottlieb, G. L., Q. Song, G. L. Almeida, D. Hong, and D. Corcos (1997): Directional Control of Planar Human Arm Movement. Journal of Neurophysiology 78:2985-2998. • Grossberg, S. and Paine, R.W.(2000): A Neural Model of Corticocerebellar Interactions During Attentive Imitation and Predictive Learning of Sequential Handwriting Movements. Neural Networks, 13, 999-1046. • Morasso, P., F.A. Mussa Ivaldi and C. Ruggiero (1983): How a discontinuous mechanism can produce continuous patterns in trajectory formation and handwriting. Acta Psychologica 54. pp. 83-98.
  23. 23. References • Sporns, O., and G.M. Edelman (1993): Solving Bernstein's problem: A proposal for the development of coordinated movement by selection. Child Dev. 64:960-981. • Saltiel, P., K. Wyler-Duda, A. d'Avella, M.C.Tresch and Bizzi, E. (2001): Muscle Synergies Encoded Within the Spinal Cord: Evidence From Focal Intraspinal NMDA Iontophoresis in the Frog. J. Neurophysiol., 85: 605-619. • Tuller, B., H. Fitch and M. Turvey (1982): The Bernstein Perspective: II. The Concept of Muscle Linkage or Coordinative Structure. in: S. Kelso (ed.): Human Motor Behavior. An Introduction. Hillsdale: Lawrence Erlbaum. • Turvey, M., H. Fitch and B. Tuller (1982): The Bernstein Perspective: I. The Problems of Degrees of Freedom and Context-Conditioned Variability. in: S. Kelso (ed.): Human Motor Behavior. An Introduction. Hillsdale: Lawrence Erlbaum. • Weiss, P. and M. Jeannerod (1998): Getting a Grasp on Coordination. News Physiol. Sci. 13. 70-75. • Zaal, F., Daigle, K., Gottlieb, G.L., Thelen, E. (1999): An unlearned principle for controlling natural movements. Journal of Neurophysiology, 82:255-259.