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Underactuated Robotics

Electrical Engineering and Computer Science MIT CC BY-NC-SA 4.0 23 lectures

Robots today move far too conservatively, using control systems that attempt to maintain full control authority at all times. Humans and animals move much more aggressively by routinely executing motions which involve a loss of instantaneous control authority. Controlling nonlinear systems without complete control authority requires methods that can reason about and exploit the natural dynamics of our machines. This course discusses nonlinear dynamics and control of underactuated mechanical systems, with an emphasis on machine learning methods. Topics include nonlinear dynamics of passive robots (walkers, swimmers, flyers), motion planning, partial feedback linearization, energy-shaping control, analytical optimal control, reinforcement learning/approximate optimal control, and the influence of mechanical design on control. Discussions include examples from biology and applications to legged locomotion, compliant manipulation, underwater robots, and flying machines. Acknowledgments Professor Tedrake would like to thank John Roberts for his help with the course and videotaping the lectures.

Syllabus

  1. 1 Lecture 1: Introduction
  2. 2 Lecture 2: The Simple Pendulum
  3. 3 Lecture 3: Optimal Control of the Double Integrator
  4. 4 Lecture 4: Optimal Control of the Double Integrator (cont.)
  5. 5 Lecture 5: Numerical Optimal Control (Dynamic Programming)
  6. 6 Lecture 6: Acrobot and Cart-pole
  7. 7 Lecture 7: Swing-up Control of Acrobot and Cart-pole Systems
  8. 8 Lecture 8: Dynamic Programming (DP) and Policy Search
  9. 9 Lecture 9: Trajectory Optimization
  10. 10 Lecture 10: Trajectory Stabilization and Iterative Linear Quadratic Regulator
  11. 11 Lecture 11: Walking
  12. 12 Lecture 12: Walking (cont.)
  13. 13 Lecture 13: Running
  14. 14 Lecture 14: Feasible Motion Planning
  15. 15 Lecture 15: Global Policies from Local Policies
  16. 16 Lecture 16: Introducing Stochastic Optimal Control
  17. 17 Lecture 17: Stochastic Gradient Descent
  18. 18 Lecture 18: Stochastic Gradient Descent 2
  19. 19 Lecture 19: Temporal Difference Learning
  20. 20 Lecture 20: Temporal Difference Learning with Function Approximation
  21. 21 Lecture 21: Policy Improvement
  22. 22 Lecture 22: Actor-critic Methods
  23. 23 Lecture 23: Case Studies in Computational Underactuated Control

Course materials