Robot Dynamics and Model-Based Control (MIT 6.8210, Lecture 1)
1:25:07Spring 2023 6.8210 Lecture 2: Nonlinear Dynamics
1:12:48Lecture 3 | MIT 6.832 (Underactuated Robotics), Spring 2020 | Dynamic Programming I
1:20:31Dynamic Programming II (MIT 6.8210, Lecture 4)
1:22:07Acrobots, Cart-poles, and Quadrotors I (MIT 6.8210/6.832 Lecture 5)
1:21:24Dynamic Programming III (MIT 6.8210, Lecture 6)
1:19:46Lyapunov Analysis I (MIT 6.8210, Lecture 7)
1:20:44Computing Lyapunov Functions I (MIT 6.8210/6.832, Lecture 8)
1:23:56Lecture 9: Computing Lyapunov Functions II (6.832)
1:21:08Lecture 10: Trajectory Optimization I (6.832)
1:18:55Lecture 11: Trajectory Optimization II (6.832)
1:22:52Lecture 12: Trajectory Stabilization (6.832)
1:18:07Lecture 15: Simple Models of Walking (6.832)
1:09:54Lecture 14: Hybrid Trajectory Optimization (6.832)
1:19:56Lecture 15: Planning + Control Through Contact (6.832)
1:16:39Lecture 16: Humanoid Robots (6.832)
1:18:47Lecture 17: Mixed-discrete (combinatorial) and continuous optimization (6.832)
1:21:04Lecture 18: Sampling-based (kinodynamic) motion planning (6.832)
1:20:47Lecture 19: Stochastic dynamics (6.832)
1:18:38Lecture 20: Stochastic Control (6.832)
1:18:37Lecture 21: Robust Control & Policy Search (6.832)
1:17:06Lecture 22: Output feedback (6.832)
1:10:41Lecture 17: Mixed-discrete (combinatorial) and continuous optimization (6.832)
This lecture introduces the formalisms and solution strategies for mixed-discrete and continuous optimization problems, examining the interplay between combinatorial decision-making and continuous variable landscapes.
Lecture 16: Humanoid Robots (6.832)
Next · Lecture 18Lecture 18: Sampling-based (kinodynamic) motion planning (6.832)