Robotics capstone: ECE 183DA/DB

Logistics

Hours

Staff

Course overview

About this course

Grading

Winter

Quarter 1

Schedule summary

Week Recorded lectures
(by end of week)
Project deliverable
(due Thu. 11am PT)
Lab checkoff
(due Thu. 6pm PT)
1 A1, C1 P1: Personal introductions L1: Git setup
2 A2, D2 P2: Team ideas L1: Mathematical formulation
3 D3, C6 (*) P2.5: Explication preparation (**) L1: Python implementation; Design decisions on sensor selection
4 A3, D4 P3: Problem explications L2: Webots setup
5 D5, D6 P4: Requirements review (RR) / System design review (SDR) L2: Design decisions on state estimator
6 C2 (*) L3: Assembly of hardware
7 P5: VC pitches L3: Design decision on mechanical design
8 L4: Experimental design for integrated comparisons
9 P6: Preliminary design review (PDR) L4: Final design decisions
10 Weekly update
11 P7: Stakeholder demo
Notes

All Project deliverables are defined in our lab git.

(*): C2 and C6 are listed out of order here, so if you have time you may want to watch C2 first to provide context for the first few minutes of C6. After that though, the two topics are independent so this order should be fine. Regardless, you’ll want to watch C6 before starting on L2.

(**): This is an ungraded but mandatory checkoff.

Lecture videos

Auxilliary videos

A1

Engineering in society

Choose (at least) one of the following:

A2

Presenting

How to plan, prepare, and give
a successful presentation

A3

Design for debugging

Design for debugging and
block diagrams (3 part lecture)

Example dependency diagrams

Design process videos

D1

Design Intro

D2

Problem Definition

D3

Requirements Definition

D4

Design Methods: Creative

D5

Design Methods: Rational 1

D6

Design Methods: Rational 2

Computational robotics videos

Box folder

C1

Lecture 1: Systems and State

Addenda / errata (pdf)

Problem set (pdf)

lec01a (29:28)

lec01b (26:02)

lec01c (26:35)

lec01d (74:20)

C2

Lecture 2: Planning / control on MDPs

Addenda / errata (pdf)

Problem set (pdf)

lec02a (34:57)

lec02b (24:39)

lec02c (40:10)

lec02d (38:58)

C3

Lecture 3: Discretization and function approximation

Addenda / errata (pdf)

Problem set (pdf)

lec03a (64:43)

lec03b (30:55)

lec03c (31:35)

lec03d (30:43)

C4

Lecture 4: Graph-search based motion planning

Addenda / errata (pdf)

Problem set (pdf)

lec04a (33:46)

lec04b (44:28)

lec04c (32:44)

lec04d (54:36)

C5

Lecture 5: Linear quadratic regulators

Addenda / errata (pdf)

Problem set (pdf)

lec05a (45:51)

lec05b (46:01)

C6

Lecture 6: Bayesian filtering and POMDPs

Addenda / errata (pdf)

Problem set (pdf)

lec06a (40:17)

lec06b (32:08)

lec06c (46:23)

lec06d (24:14)

C7

Lecture 7: Kalman filtering and SLAM

Problem set (pdf)

lec07a (58:25) - kalman filterkf slides (pdf)kf notes (pdf)

lec07b (39:34) - SLAMslam slides (pdf)

lec07c (28:00) - Active SLAM concepts

lec07d (23:07) - Active SLAM papers

C8

Lecture 8: Reinforcement learning

Addenda / errata (pdf)

Problem set (pdf)

lec08a (59:28)

lec08c (49:00)

lec08b (35:04)

C9

Lecture 9: Imitation learning, gaussian processes

Addenda / errata (pdf)

Problem set (pdf)

lec09a (42:48)

lec09c (54:57)

lec09b (43:11)

C10

Lecture 10: Other topics in robotics

lec10 (1:18:02)