Welcome to CS237A!

AA 274a / CS 237A / EE 260A

Principles of Robot Autonomy I

Fall 2025

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).

Instructors

Mac Schwager

Course Assistants

Rhea Malhotra	Aarya Sumuk	Esteban Rincon	Naixiang Gao
Purush Mani	Polo Contreras	John Tucker	Jingyun Yang

Lecture Times

Tue, Thu 1:30 PM - 2:50 PM at Skilling Auditorium

Students are expected to attend one 2-hour section each week. Details about lab signups will be posted Week 2.

Office Hours

📚 CA Office Hour Schedule
If you're having trouble with ROS/UTM or your personal machine, you are welcome to use the Skilling Lab computers during any office hours or any lab section to complete your homework. For missing labs, please attend office hours to make up the lab and get checked off.

For urgent questions, email the staff mailing list at cs237a-aut2526-staff@lists.stanford.edu

Syllabus

The class syllabus is available here.

Resources

EdStem -- for discussions and questions.
Gradescope -- for homework and project submissions.
Overleaf LaTeX Template -- for homework and reports.
Canvas -- for course content, recordings, and announcements.

Exams

Exams for this course will be in a take-home format. The midterm exam will be a take-home given in Week 6 of the course (details on distribution and submission times will be announced in lecture). The exam will be open for a 48 hour period, in which a 5 hour window can be used to take the exam. The Final exam, also a take-home, will be sent out on the first day of finals week, and due at the end of the official scheduled final time on Gradescope (Tuesday December 9, at 6:30 PM PST).

Course Grade Calculation

20% – Homeworks (5 × 4% each)
40% – Sections (8 × 5% each)
15% – Midterm
25% – Final

Tentative Schedule

Date	Topic	Homework	Lab
09/23	Course overview, perception-action loop, maps		Lab 0: Install ROS
09/25	Maps, Robot geometry, Coordinate frames and SE(2)/SE(3) transforms	HW1 out
09/30	Collision, C-space, motion models. Path planning I: A*		Lab 1: Command line, Git, Python
10/02	Path planning II: RRT, RRT*
10/07	Trajectory optimization	HW1 due, HW2 out	Lab 2: ROS basics
10/09	Trajectory following: PID, LQR, gain scheduled LQR
10/14	Robotic sensors: IMU, lidar, cameras, RGB-D. Point clouds & ICP		Lab 3: RViz, Turtlebot
10/16	Pinhole camera models, camera calibration	HW2 due, HW3 Out
10/21	Structure from Motion (SfM), features, RANSAC		Lab 4: Heading controller
10/23	Learning based perception, semantic perception
10/28	SLAM intro, factor graphs, PGO	HW3 due	Lab 5: Nav to goal
10/29	Midterm: 48 hour window	Midterm out
10/30	Pose graph opt, bundle adjustment
10/31	Midterm: 48 hour window window	Midterm due
11/04	No Lecture (Democracy Day)	HW4 out
11/06	Bayes Rule, RVs, Occ. mapping (Recorded Lecture, Mac traveling)
11/11	Occ, mapping, frontier exploration		Lab 6: Object detection
11/13	Gaussian RVs, Kalman Filtering, EKF, UKF	HW4 due, HW5 out
11/18	Particle Filtering, Monte Carlo localization		Lab 7: Frontier exploration
11/20	Guest Lecture
11/25	No lecture (Thanksgiving)
11/27	No lecture (Thanksgiving)
12/02	EKF Localization, obj tracking	HW5 due	Lab 8: Makeup
12/04	Advanced Topics: Imitation learning, VLAs, 3DGS for sim2real, world models
12/07	48 hour window take home final start	Final Exam out, 6:30pm
12/09	48 hour window take home final start	Final Exam due, 6:30pm

Follow this link to access the course website for the previous edition of Principle of Robot Autonomy I, Fall 2024.