CS 395T: Robot Learning



In this course, we will study the problem of how an agent can learn to perceive its world well enough to act in it and make reliable plans. Studying this problem in the context of physical robots forces us to confront the continuous nature of sensory input, of action, and of the environment in which the agent exists. Our goal is to determine how the agent can discover useful abstractions from continuous sensorimotor experience, to build symbolic knowledge representations describing its world.

This problem is of practical importance, because the robots of the future will have increasingly rich sensory systems. It will be necessary for them to learn from their own experience how their sensors respond to the world and how the world responds to the actions they take. The meanings of symbols in their knowledge representation are then "grounded" in the nature of their sensorimotor interaction with the environment. In addition to its practical importance, this problem is central to the philosophical problem of whether artificial intelligence is possible at all (Searle, Harnad).

We will start from a theory of spatial exploration, mapping, and navigation in unknown environments to define a target for the agent's learning process. We will examine previous work (by Gary Drescher, David Pierce, Wei-Min Shen, Paul Cohen, Luc Steels, Rob Schapire, Pat Langley, etc.) on learning from an unknown sensory world.

Experiments and assignments will be done on simulated and physical robots. One of our physical robots is a prototype of an intelligent wheelchair.


This is a research seminar, intended first to bring you to the state of the art, and then to help you do a project and paper of publishable quality. There will be a significant amount of reading of research papers that will be handed out.

There will be several mathematical and programming assignments, a term project and a presentation.


Each class member will select a topic and present the material to the class. Each topic will have an associated reading that the entire class will read, but the presenter is responsible for finding and reading additional material, becoming an expert in the area, creating an illuminating example to present, and leading a discussion. Plan to give a 25 minute presentation, followed by questions and discussion of the value and importance of the material presented.

Presentation topics

(We may spend multiple sessions on some of these.) Obviously, it will be helpful to pick a presentation topic that works well with your term project topic.

Term Projects

Each class member will do a term project. You can apply a method we are learning about to a robot learning problem. Or you can extend an existing method or develop a new method to solve a problem. Ideally, your term project will extend the state of the art, and will be suitable for submission to AAAI or ICRA or some other major conference.

You are encouraged to select a topic that fits well with your other research interests.


The textbooks are references that will be valuable in different ways in this area. The University Coop may not have these in time, so you might consider buying them from Amazon or Barnes & Noble. You will also need to do assignments in either MATLAB or LabVIEW. Make sure you have any documentation you need.

If you do not already have a background in Artificial Intelligence, you should invest in the following excellent textbook. It's another valuable addition to your library, and is undoubtedly available used.

Resources (check for updates)

The following resources are from last year's CS 395T: Intelligent Robotics, which had a significantly different focus. These links will be updated, but you might still find them interesting.

Students with Disabilities

The University of Texas at Austin provides upon request appropriate academic accommodations for qualified students with disabilities. For more information, contact the Office of the Dean of Students at 471-6259, 471-4641 TTY.