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)
1:10:41Computing Lyapunov Functions I (MIT 6.8210/6.832, Lecture 8)
Toyota Professor of EECS, Aeronautics & Mechanical Engineering
This lecture introduces the foundational concept of Lyapunov functions for analyzing stability in nonlinear control systems, focusing on methods for their computational synthesis. The session covers stability definitions, invariant set theorems, and initial approaches to constructing Lyapunov functions via convex optimization.
Lyapunov Analysis I (MIT 6.8210, Lecture 7)
Next · Lecture 9Lecture 9: Computing Lyapunov Functions II (6.832)