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  • Lesson.No : 17
  • 01:21:04

  • 6.8210 Spring 2024 Lecture 17: Mixed-discrete (combinatorial) and continuous optimization

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Course Lessons

  1. 1- 6.8210 Spring 2024 Lecture 1: Robot dynamics and model-based control
  2. 2- Spring 2023 6.8210 Lecture 2: Nonlinear Dynamics
  3. 3- Lecture 3 MIT 6.832 (Underactuated Robotics), Spring 2020 Dynamic Programming I
  4. 4- 6.8210 Spring 2024 Lecture 4: Dynamic Programming II
  5. 5- 6.8210 Spring 2024 Lecture 5: Acrobots, Cart-poles, and Quadrotors I
  6. 6- 6.8210 Spring 2024 Lecture 6: Dynamic Programming III
  7. 7- 6.8210 Spring 2024 Lecture 7: Lyapunov Analysis I
  8. 8- 6.8210 Spring 2024 Lecture 8: Computing Lyapunov Functions I
  9. 9- 6.8210 Spring 2024 Lecture 9: Computing Lyapunov Functions II
  10. 10- 6.8210 Spring 2024 Lecture 10: Trajectory Optimization I
  11. 11- 6.8210 Spring 2024 Lecture 11: Trajectory Optimization II
  12. 12- 6.8210 Spring 2024 Lecture 12: Trajectory Stabilization
  13. 13- Lecture 15: MIT 6.832 Underactuated Robotics (Spring 2022) "Simple Models of Walking"
  14. 14- 6.8210 Spring 2024 Lecture 14: Hybrid Trajectory Optimization
  15. 15- 6.8210 Spring 2024 Lecture 15: Planning + Control Through Contact
  16. 16- 6.8210 Spring 2024 Lecture 16: Humanoid Robots
  17. 17- 6.8210 Spring 2024 Lecture 17: Mixed-discrete (combinatorial) and continuous optimization
  18. 18- 6.8210 Spring 2024 Lecture 18: Sampling-based (kinodynamic) motion planning
  19. 19- 6.8210 Spring 2024 Lecture 19: Stochastic dynamics
  20. 20- 6.8210 Spring 2024 Lecture 20: Stochastic Control
  21. 21- 6.8210 Spring 2024 Lecture 21: Robust Control & Policy Search
  22. 22- 6.8210 Spring 2024 Lecture 22: Output feedback
  23. 23- 6.8210 Spring 2024 Lecture 23: Feedback motion planning
  24. 24- 6.8210 Spring 2024 Lecture 24: Imitation learning / Foundation models / Course wrap-up