| Week | Topic | Lecture | Pset | Project milestone |
|---|---|---|---|---|
| 0 | About this course | pset 0 | Introductions: Communicate your personal interests, abilities, and skills | |
| 1 | Systems and State | C1 | pset 1 | Teaming: find 2-4(ish) people with common interests |
| 2 | Planning / control on MDPs | C2 | pset 2 | Project exploration: Develop 3-5(ish) project ideas |
| 3 | Discretization and function approximation | C3 | pset 3 | Project proposal: Specify project expectations and schedule |
| 4 | Graph-search based motion planning | C4 | pset 4 | Project outline: Identify project solution and narrative |
| 5 | Linear quadratic regulators | C5 | pset 5 | Mathematical formulation: Develop relevant mathematical models |
| 6 | Bayesian filtering and POMDPs | C6 | pset 6 | Infrastructure: Set up computational tools / environment |
| 7 | Kalman filtering and SLAM | C7 | pset 7 | Benchmarking: Establish current state-of-the-art capabilites |
| 8 | Reinforcement learning | C8 | pset 8 | Initial results: Demonstrate solution process |
| 9 | Imitation learning, gaussian processes | C9 | pset 9 | Final results: Validate and support successful contributions |
| 10 | Society, sociology, humanity; current state of robotics research | C10 | pset 10 | Communication: Refine presentation of contributions in oral and written forms |
Lecture 1: Systems and State
Lecture 2: Planning / control on MDPs
Lecture 3: Discretization and function approximation
Lecture 4: Graph-search based motion planning
Lecture 5: Linear quadratic regulators
Lecture 6: Bayesian filtering and POMDPs
Lecture 7: Kalman filtering and SLAM
lec07a (58:25) - kalman filter — kf slides (pdf) — kf notes (pdf)
lec07b (39:34) - SLAM — slam slides (pdf)
Lecture 8: Reinforcement learning
Lecture 9: Imitation learning, gaussian processes
Lecture 10: Society, sociology, humanity; state of current robotics research
Presenting
Engineering in society
Choose (at least) one of the following:
Design for debugging
Design for debugging (3 part lecture)