CS 123: A Hands-On Introduction to Building AI-Enabled Robots

Pupper Robot

**This is a previous offering of the course. Navigate here for the current offering

2024-2025 Fall Teaching team:

• Instructors: Prof. Karen Liu (Stanford CS), Jie Tan (Google DeepMind), Stuart Bowers (Apple, Hands-On Robotics)

• Co-Instructors: Wenhao Yu (Google DeepMind), Tingnan Zhang (Google DeepMind)

• Head TAs: Ankush Dhawan (MS EE 2025), Zipeng Fu (PhD CS 2025)

• TAs: Jaden Clark (CS 2024), Josh Francis (CS 2024), Akemi Sabatier (ME 2026), Zara Rutherford (EE 2026),

Overview:

Welcome to the course page for Stanford’s course in legged robots!

This course offers a hands-on introduction to AI-powered robotics. Unlike most introductory robotics courses, students will learn essential robotics concepts by constructing a quadruped robot from scratch and training it to perform real-world tasks such as navigation and command following. The course covers a broad range of topics critical to robot learning, including motor control, forward and inverse kinematics, system identification, simulation, and reinforcement learning. Through weekly labs, students will construct and program an agile robot quadruped named Pupper. In the final few weeks, students will undertake an open-ended project, such as training Pupper to perform agile movements, developing a vision system to allow Pupper to play fetch, or adapting large language models to enable Pupper’s ability to communicate with humans.

“Empowering robots with AI is essential to make them smart and useful in people’s daily life. It is one of the most important research directions in both academia and industry. This class teaches the most relevant skills, gives students hands-on experiences, and prepares them for a career in the area of AI and robotics.” - Jie Tan, Staff Research Scientist at Google DeepMind

Time: Monday, 3:30-6:20pm

Lecture Location: 320-109, in-person attendance required

Instructor Office Hours:

• Karen: 9:15-10:15am Th, Gates 368

• Stuart, Jie, Wenhao, Tingnan: Office hours by appointment. Reach out to the teaching team to schedule.

TA Office Hours Location: Gates B08

TA Office Hours:

• Jaden: 9:30-11:00 AM M

• Josh: 7:00-8:30 Tu

• Akemi: 4:00-5:30 W

• Ankush:: 7:00-8:30 W

• Zara: 4:00-5:30 Th

• Zipeng: 5:00-6:30 F

Prerequisites:

• CS106B (programming will be majority in Python)

• CS107 (familiarity with the command line)

• MATH51/CME100 (understanding of gradients)

• No robotics experience necessary!!

Number of credits: 3

Grading: Students will work in assigned group for all labs, but are free to from their own groups for the final project. Everyone in the same group will receive the same score for each lab. Some labs have written homework for which students get individual credits.

Lab Policies: Lab: Labs are due before class the following week they are assigned (3:30, Mondays) unless stated otherwise. Each team has a total of 7 late days over all labs. Using 1 late day extends the deadline by 24 hours. A team may use up to 3 late days per lab. No lab will be accepted more than 72 hours after the deadline. Individual written assignments: Each student has a total of 5 late days over all written assignments. Using 1 late day extends the deadline by 24 hours. You may use up to 3 late days per assignment. No assignment will be accepted more than 72 hours after the deadline. Final project: no extension is allowed for the final project proposal and presentation.

Enrollment: 36 students; 6 groups of 6

Schedule

	Lecture	Lab	Lab Due Date	Other
Week 1: 9/23	ROS Introduction and PD Control	Lab 1: ROS Introduction and PD Control	9/30
Week 2: 9/30	Forward Kinematics	Lab 2: Forward Kinematics	10/07
Week 3: 10/7	Inverse Kinematics	Lab 3: Inverse Kinematics and Trajectory Tracking	10/14
Week 4: 10/14	Gait Control	Lab 4: Model-Based Control and Trotting Gait Implementation	10/21
Week 5: 10/21	Reinforcement Learning for Robotics	Lab 5: How to Train Your Dog	10/28
Week 6: 10/28	Large Language Models for Robotics	Lab 6: Do What I Say	11/11
Week 7: 11/4	Computer Vision for Robotics	Lab 7: Seeing is Believing	11/11
Week 8: 11/11	No Lecture! Open Lab Time	Meet with TAs about Final Projects		Project Proposals due 11/15 at 11:59
Week of 11/18	No Lecture! Open Lab Time	Work on Final Projects!
Week 9: 11/25	Thanksgiving Break!	No Class/Lab!
Week 10: 12/2	No Lecture! Open Lab Time	Work on Final Projects		Final Project checkpoint 12/2
Finals Week	Dog Show on 12/11 7-10pm	Demo your final Projects!

References: References Page