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 22: Output feedback (6.832)
This lecture examines control strategies that rely solely on measured outputs rather than full state information, discussing observer design and the separation principle. It details how to achieve stability and performance with incomplete state knowledge.
Seminar notes & discussion
No public seminar notes on this lecture yet.