Syllabus

Pupper

Pupper Robot

A hands-on introduction to building AI-enabled robots.

Teaching team:

  • Nathan Kau (MSME 2022, Stanford Student Robotics)

  • Gabrael Levine (CS 2023, Stanford Student Robotics)

  • Abdulwahab Omria (CS 2023, Stanford Student Robotics)

  • Raghav Samavedam (CS 2024, Stanford Student Robotics)

  • Stuart Bowers (Hands-On Robotics)

  • Jie Tan (Google Brain)

Overview:

In the first six weeks, students will learn key robotics concepts like including motor control, forward and inverse kinematics, and system identification; as well as important embodied-AI concepts including reinforcement learning and simulation. Through weekly labs, students will build a pair of teleoperated robot arms with haptic feedback, program a robot arm to learn to move by itself, and most importantly, build and program an agile robot quadruped called Pupper (pictured above). In the last four weeks of the course, students will pursue an open-ended project using Pupper as a platform, such as teaching Pupper to walk using reinforcement learning, building a vision system so Pupper can play fetch, or redesigning the hardware to make the robot more agile.

Researchers from Google Brain will give a guest lecture during the quarter on their work teaching robots new skills using reinforcement learning.

“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 hand-on experiences, and prepares them for a career in the area of AI and robotics.” - Jie Tan, Staff Research Scientist at Google Brain

Expected time commitment: 6 - 8 hours per week.

Estimated class size: 8 - 12 students

Prerequisites: CS106B or similar coding experience strongly recommended. Coding will be majority Python but some C++ (Arduino). Familiarity with the command line. Math 51 or CME 100 or equivalent understanding of gradients. No robotics experience necessary!!

Grading: Pass/Fail for 2 or more units. Grading based on participation.

Spring Quarter Faculty sponsor: Professor Karen Liu