SES # | TOPICS | KEY DATES |
---|---|---|
1 |
Fully- vs. under-actuated systems Preliminaries | |
2 | Nonlinear dynamics of the simple pendulum | Problem set 1 out |
3 |
Introduction to optimal control Double-integrator examples | |
4 |
Double integrator (cont.) Quadratic regulator (Hamilton-Jacobi-Bellman (HJB) sufficiency), min-time control (Pontryagin) | |
5 | Dynamic programming and value interation: grid world, double integrator, and pendulum examples |
Problem set 1 due Problem set 2 out |
6 | Acrobot and cart-pole: controllability, partial feedback linearization (PFL), and energy shaping | |
7 | Acrobot and cart-pole (cont.) | |
8 | Policy search: open-loop optimal control, direct methods, and indirect methods | Problem set 2 due |
9 | Policy search (cont.): trajectory stabilization, iterative linear quadratic regulator (iLQR), differential dynamic programming (DDP) | Problem set 3 out |
10 | Simple walking models: rimless wheel, compass gait, kneed compass gait | |
11 | Feedback control for simple walking models | |
12 | Simple running models: spring-loaded inverted pendulum (SLIP), Raibert hoppers | |
Midterm | ||
13 | Motion planning: Dijkstra's, A-star | Problem set 3 due |
14 | Randomized motion planning: rapidly-exploring randomized trees and probabilistic road maps | Problem set 4 out |
15 | Feedback motion planning: planning with funnels, linear quadratic regulator (LQR) trees | |
16 | Function approximation and system identification | Final project proposal due |
17 | Model systems with uncertainty: state distribution dynamics and state estimation | Problem set 4 due |
18 | Stochastic optimal control | Problem set 5 out |
19 | Aircraft | |
20 | Swimming and flapping flight | Problem set 5 due |
21 | Randomized policy gradient | |
22 | Randomized policy gradient (cont.) | |
23 | Model-free value methods: temporal difference learning and Q-learning | |
24 |
Actor-critic methods Final project presentations | |
25 | Final project presentations | Final projects due |