Course Information

Robot Learning - Spring 2026

Course Description

An intelligent robotic agent is required to be able to adapt and learn from new environments, tasks, and situations through interactions with its surroundings. Robot learning is a field that combines robotics and artificial intelligence (AI) to develop technologies for robotic agents that can acquire new skills and knowledge through experience, similar to humans. Robot learning encompasses various techniques and approaches that enable robots to become more autonomous and capable of performing tasks without explicit programming for all possible scenarios. In this course, we will review recent advances in robot learning, including imitation learning and deep reinforcement learning. We will first review Markov decision processes (MDP) and reinforcement learning. Then we will discuss recent developments in imitation learning, deep learning, and deep reinforcement learning, including topics such as behavior cloning, inverse reinforcement learning, policy gradient, deep Q-network (DQN), generative adversarial imitation learning, maximum entropy reinforcement learning, safe reinforcement learning, and offline reinforcement learning. Recent advances in robot foundation models will be discussed as well. This is an advanced graduate course and substantial reading and programming projects will be assigned. Students are expected to participate actively in class. Lectures will be in English.

Announcements

• [02/23] Please read Ethics of Learning.

Schedule

• Week 1:

  • 03/04: Introduction
    

    • Ch. 3 from Reinforcement Learning: An Introduction; Ch. 16 from AIMA

• Week 2:

  • 03/09
  • 03/11

References

• Reinforcement Learning: An Introduction (2018, 2nd Edition) Richard S. Sutton, Andrew G. Barto
• Artificial Intelligence: A Modern Approach (4th edition), Stuart Russell and Peter Norvig, Prentice Hall, 2022. (AIMA Website)
• Gaussian Processes for Machine Learning, Carl Edward Rasmussen and Christopher K. I. Williams, The MIT Press, 2006.

Prerequisites

• (430.457) Introduction to Intelligent Systems (지능시스템개론).
• Also requires strong background in algorithms, linear algebra, probability, and programming.