Course Meeting Times

Lectures: 2 sessions / week, 1.5 hours / session

Course Staff

Prof. Brian Williams


Modern and future robots will have enough computational horse power to be able to reason continuously and deeply about themselves and their environments. In this advanced graduate course we will study the models and algorithms underlying recent robotic successes, ranging from the Mars Exploration Rover and similar remote systems, to the Nursebot, Museum Tourguide, and similar human-interaction systems. We will discuss the theory that underlies these algorithms and how they are implemented on real systems.

This year potential course projects include the creation of a fully autonomous Mars rover, using NASA's Mars Rover simulator, and daily mission scenarios provided by the JPL Mars Exploration Rover science team.

Algorithms and paradigms for creating a wide range of cognitive systems that act intelligently and robustly, by reasoning extensively from models of themselves and their world. Examples include a wide range of embedded and robotic systems, including autonomous Mars explorers, cooperative vehicles, and human interaction systems. Topics include deduction and search in real-time; temporal, decision-theoretic and contingency planning; dynamic execution and re-planning; reasoning about hidden state and failure; reasoning under uncertainty, path planning, mapping and localization, and cooperative and distributed robotics.


6.041 and either 16.410, 16.413, 6.034, or 6.825. Programming proficiency assumed.