Course Description

This course will cover basic principles for endowing mobile autonomous robots with planning, perception, and decision-making capabilities. Algorithmic approaches for trajectory optimization; robot motion planning; robot perception, localization, and simultaneous localization and mapping (SLAM); state machines. Extensive use of the Robot Operating System (ROS) for demonstrations and hands-on activities. Prerequisites: CS 106A or equivalent, CME 100 or equivalent (for calculus, linear algebra), and CME 106 or equivalent (for probability theory).

Instructor

Marco Pavone

Course Assistants

Milan Ganai	Jacky Kwok	Jenny Kim

Meeting Times

Lectures meet on Tuesdays and Thursdays from 10:30am to 11:50am at CoDa B60.

Students are expected to attend one 2-hour section each week. Check announcements for more details

Prof. Pavone's office hours are on Tuesdays 1:00 - 2:00pm in Durand 261 and by appointment.

CA office hours:

• Wednesdays, 4:30 - 6:30pm in Durand 270
• Thursdays, 4:30 - 6:30pm in Durand 270
• Note: Office hours will be held in Durand 114 on October 2 and November 6

Syllabus

The class syllabus can be found here.

Links

Canvas -- for course content, recordings, and announcements.
Gradescope -- for homework and project submissions.
Edstem -- for discussions and questions.
Section Sign-up -- sign up for a section time slot by 12 PM on Thursday, September 25^th.

Resources

The homeworks require you to set up a local ROS2 environment and install vairous packages. Follow this guide to set up environment on your local machine. Additional recommended reading material:

D. Gammelli, J. Lorenzetti, K. Luo, G. Zardini, M. Pavone. Principles of Robot Autonomy. Pre-print, 2025. Available at: https://stanfordasl.github.io/PoRA-I/aa174a_aut2526/resources/PoRA.pdf

Paper Review

Taking this class for 4 units entails additionally completing a paper review by the end of the quarter. See the paper review requirements here.

Schedule

Subject to change. We will try to have the lecture slides and notes uploaded before each class period.

Week	Topic	Lecture Slides	Jupyter Notebooks	Sections
1	Course overview, intro to robotic systems and ROS Fundamentals of ROS Friday: HW1 out	Lecture 1 Lecture 2 Lecture 2 Recording Pre-knowledge quiz		Section 1 Handout Section 1 Slides
2	State space dynamics — definitions and modeling State space dynamics — computation and simulation	Lecture 3 Lecture 4	Notebook 3 & 4	Section 2 Handout Section 2 Slides
3	Trajectory optimization Trajectory tracking & closed-loop control Tuesday: HW2 out Friday: HW1 due	Lecture 5 Lecture 6	Notebook 5.1 Notebook 5.2	Section 3 Handout Section 3 Slides
4	Graph search algorithms Sampling-based motion planning	Lecture 7 Lecture 8 Practice Midterm Practice Midterm Solution	Notebook 8	Section 4 Handout Section 4 Slides
5	Robotic sensors & introduction to computer vision Camera models & coordinate frames Monday: HW2 due Tuesday: HW3 out	Lecture 9 Lecture 10	Notebook 9 Notebook 10	Section 5 Handout
6	Image processing, feature detection, and feature description Information extraction Friday: HW3 due, HW4 (part 1) out	Lecture 11 Lecture 12	Notebook 11 Notebook 12	Section 6 Handout
7	Tuesday: No lecture (Democracy Day) Thursday: In-class midterm			Section 7 Handout
8	Deep learning for computer vision Intro to state estimation & filtering theory Friday: HW4 (part 1) due, HW4 (part 2) out	Lecture 13 Lecture 14		Section 7 Handout
9	Parametric filtering (KF and EKF) Markov localization and EKF-localization	Lecture 15 Lecture 16	Notebook 15 Notebook 16	Section 7 Handout
N/A	Thanksgiving Break
10	Multi-sensor perception & sensor fusion Simultaneous localization and mapping (SLAM) Tuesday: HW4 (part 2) due	Lecture 17 (part 1) / Lecture 17 (part 2) Lecture 18