Computational Principles of Sensorimotor Control - Lecture 1

Computational Principles of Sensorimotor Control - Lecture 1

International Centre for Theoretical Sciences via YouTube Direct link

Impedance

31 of 41

31 of 41

Impedance

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Computational Principles of Sensorimotor Control - Lecture 1

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  1. 1 Complexity of human movement control
  2. 2 Modest success in robotics: Manipulation
  3. 3 Normative approach to human movement control
  4. 4 Reverse-engineering sensorimotor control
  5. 5 Motor planning
  6. 6 Arm movements: Paths
  7. 7 Eye movements: saccades
  8. 8 Models
  9. 9 The Assumption of Optimality
  10. 10 The ideal cost for goal-directed movement
  11. 11 Motor noise is signal-dependent
  12. 12 Signal-dependent noise and optimal control
  13. 13 Pointing movements: minimize variability
  14. 14 Motor control in the late
  15. 15 The demise of the desired trajectory
  16. 16 Motor control in the early
  17. 17 Optimal Feedback Control Todorov, Kappen
  18. 18 Optimal control and planning
  19. 19 State estimation Interpreting the uncertain state of the world
  20. 20 Generative model of state evolution
  21. 21 Kalman filter is the Bayesian estimator
  22. 22 Motor prediction with forward model
  23. 23 How is eye position estimated
  24. 24 Motor prediction
  25. 25 Types of Kalman estimation problems
  26. 26 Minimizing delays
  27. 27 Types of Motor Learning
  28. 28 Representations in motor learning
  29. 29 Mechanistic models
  30. 30 Normative models
  31. 31 Impedance
  32. 32 Measuring stiffness
  33. 33 Controlling stiffness
  34. 34 Bayesian Decision Theory
  35. 35 Sensorimotor learning and Bayes rule
  36. 36 Loss Functions in movement
  37. 37 Virtual pea shooter
  38. 38 Predictions
  39. 39 Loss function is robust to outliers
  40. 40 Imposed loss function
  41. 41 Summary

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