CIS700-Real-World-Robot-Learning

Research & Idea Blogs

Tony Wang
02/02/2025 – now

Spring 2025. ESE 6800/CS7000. Tue / Thu 10:15-11:45

Course Overview

Decision-making has been a cornerstone of artificial intelligence since the field’s inception in the 1960s. While the techniques and algorithms have evolved dramatically over time, the fundamental challenge remains: how to make intelligent decisions in the presence of uncertainty. Over the past six decades, this research has led to the development of highly advanced systems, with some achieving superhuman performance in cognitively demanding tasks like Go, Atari, Gran Turismo, Chess, StarCraft, and SOTA.

However, despite these remarkable successes, most of these systems excel only in controlled, simulated, or game-based environments. Why haven’t the same methods translated seamlessly to real-world decision-making, such as controlling a physical robot to perform household tasks? What makes real-world environments so uniquely challenging? 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 techniques for learning-based decision-making, such as imitation learning and reinforcement learning, focusing on their practical challenges when applied in real-world scenarios. Through a combination of lectures, student presentations, hands-on projects, 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.

Overture

Jan. 16

Intro + OverviewSyllabus, Slides
Jan. 21

Intro To Imitation Learning & Reinforcement Learning Part ISlides
Jan. 23

Intro To Imitation Learning & Reinforcement Learning Part IISlides
Jan. 28

Intro To Imitation Learning & Reinforcement Learning Part IIISlides
Jan. 30

Hands-on Tutorial on Policy Learning (Lab)Bring your laptop!IL Tutorial Colab, RL Tutorial Colab