CS 395T: Intelligent Robotics


Syllabus

In this seminar, 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.

Closely related to this is the problem of a long-lived and complex robot adapting to the inevitable changes in its sensorimotor system. Some sensors will fail. Others will drift out of calibration. It may be possible for certain sensors to learn to improve their sensing ability over time. And new sensors, and even entirely new sensory modes, could be plugged in to the robot. How does it adapt to these changes, by learning from its own experience?

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).

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

Assignments

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.

Presentations

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.

Pick a presentation topic that works well with your term project topic. (We may spend multiple sessions on some of these.)

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.

Topics for Presentations and Projects

I will present various aspects of the research currently going on in our group. Project and presentation topics will deal with other approaches to the same problem, and may involve integrating their best properties into our framework.

Representing and learning control laws

Learning sensorimotor features

Recent steps in robot mapping

Cognitive maps in rats and humans

This list will be fleshed out with more references, and very likely more topics, by the time the class starts.

Textbooks

These are some useful books that you should have in your professional library, and that are related to this course. I will assume that you have immediate access to material in these books. 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. You will also need to do assignments in either MATLAB or LabVIEW. Make sure you have any documentation you need.

The following monograph is, I believe, an important early exploration of some of the problems we want to solve. It contains some significant pieces of the puzzle, but we don't yet know how to re-use them. We will definitely discuss it. Buy it if you want to do research in this area.


Resources (check for updates)

You may find interesting resources in the syllabus for CS 395T: Robot Learning, which was taught in Fall 2001.
BJK