George Konidaris
Director: Intelligent Robot Lab
Associate Professor
Department of Computer Science
Brown University, Providence RI

Welcome to my home page. I'm an Associate Professor of Computer Science and director of the Intelligent Robot Lab at Brown, which forms part of bigAI (Brown Integrative, General AI). My group and I conduct research driven by the overarching scientific goal of understanding the fundamental computational processes that generate intelligence, and using them to design a generally-intelligent robot.

I am also the co-founder of two technology startups. I co-founded, and serve as the Chief Roboticist of, Realtime Robotics, a startup based on our research on robot motion planning, and that aims to make robotic automation simpler, better, and faster. I also co-founded Lelapa AI, a commercial AI research lab focused on technology by and for Africans, and based in Johannesburg, South Africa.

If you're considering applying to the PhD program at Brown to study in my lab, please see this page.


My research aims to build intelligent, autonomous, general-purpose robots that are generally capable in a wide variety of tasks and environments.

I focus on understanding how to design agents that learn abstraction hierarchies that enable fast, goal-oriented planning. I develop and apply techniques from machine learning, reinforcement learning, optimal control and planning to construct well-grounded hierarchies that result in fast planning for common cases, and are robust to uncertainty at every level of control.

I believe that it will take advances in all of these areas, and additionally advances in how to integrate these areas, to solve the AI problem.

You can find an approachable (and short!) summary to some of my recent thinking on how to build generally intelligent agents in the following review paper:

  • G.D. Konidaris. On The Necessity of Abstraction. Current Opinion in Behavioral Sciences 29 (Special Issue on Artificial Intelligence), pages 1-7, October 2019.

... and in my recent invited talk at CoRL 2019 in Osaka, which is a good summary of my lab's work over the last few years:

This recent journal paper is a good indicator of my interests - it combines ideas from hierarchical reinforcement learning, probabilistic machine learning, task-level planning, and robotics to create a robot that autonomously learns an abstract symbolic model of an environment and then uses it to plan:

Of course, the video is a lot more accessible:

Other than that, here are a few sample project pages:


I am currently teaching:

I have previously taught the following classes at Brown:

I taught the following classes when I was at Duke: