Physical Intelligence

Fall 2025. ESE 6510. Tue / Thu 10:15-11:45. Fagin Hall 118.

Announcements

First day of class.

Aug 26 · 0 min read

I will be giving a talk at the Meta workshop on egocentric perception on Aug 25-26. The first day of class will be after the workshop.

Course Overview

Why don’t we yet have a foundation model that can operate robots in the physical world? What makes applying the standard pre-training approach so uniquely challenging in robotics? And what recent advances are pushing the boundaries of what’s possible in real-world applications?

This course offers a structured framework to explore these questions. We will study the mathematical foundations of techniques for learning-based decision-making and examine how these methods are deployed in robotic systems. Through a combination of lectures, hands-on projects (including a drone race!), and guest presentations from leading experts in the field, students will gain a deep understanding of the state-of-the-art decision-making systems and their challenges when applied to robotics.

Prerequisites

This is a graduate-level course. Students are expected to have prior knowledge in deep learning and robotics, such as the topics covered in Robot Learning (ESE 650), Applied Machine Learning (CIS 5190), and Introduction to Robotics (MEAM-520).

Schedule

Foundation

AUG 28

Intro: Syllabus,; Slides

SEP 2

What is a Robot? Actuation: Slides

SEP 4

What is a Robot? Perception: Slides

SEP 9

Intro to Probability I: Notes (class content was slightly different)

Reinforcement Learning

SEP 11

Intro to Reinforcement Learning: Slides

SEP 16

Intro to Reinforcement Learning: Slides

SEP 18

Policy Gradient: Shulman, Ch. 2.6

SEP 23

Policy Gradient (Advantage Estimation): Shulman, Ch. 4

SEP 25

Tutorial on RL: Slides

SEP 30

Advanced Policy Gradient (TRPO,PPO): Shulman, Ch. 3, PPO

OCT 2

Policy Gradients and World Models: Slides

OCT 7

Value and Q Learning: S&B, Ch. 4; Ch. 5(up to 5.5); Ch. 6(6.5,6.7)

Sim2Real

OCT 9

No Class Fall Break :fallen_leaf:

OCT 14

Building a Simulator: Slides

OCT 16

Sim2Real: Intro & Basics: Slides, Race challenge is out

OCT 21

Sim2Real: Tips & Tricks and Intro to Imitation Learning: Slides

Imitation Learning

OCT 23

Learning from Pre-Trained Models (Behavioral Models): Slides

OCT 28

Imitation Learning as Generative Modeling: Flow-matching, Diffusion: Slides

OCT 30

MidTerm

Applications and Frontiers

NOV 4

Guest Lecture I: Homanga Bharadhwaj

NOV 6

Guest Lecture II: Rachel Holladay

NOV 11

Tutorial on Imitation Learning: Slides

NOV 13

Guest Lecture III: Yunzhu Li

NOV 18

Guest Lecture IV: Haozhi Qi, Submission Deadline for Race Phase I.

NOV 20

Challenges ahead: Slides

NOV 25

The Illusion of Intelligence: Slides

NOV 27

No Class Thanksgiving :turkey:

DEC 2

Recitation

DEC 4

Race Day (Phase II)

Instructors

Robots that learn, UC Berkeley.

Robotics Manipulation, MIT.

Deep Reinforcement Learning: CMU version UC Berkeley version.

Introduction to Robot Learning, CMU.

Embodied AI Safety, CMU. This course is not only very interesting, but also has an awesome webpage.