Glen Berseth

Glen Berseth

I am an assistant professor at the University de Montreal and Mila. My research explores how to use deep learning and reinforcement learning to develop generalist robots.

I am an assistant professor at the Université de Montréal, a core academic member of the Mila - Quebec AI Institute, CIFAR AI chair, and co-director of the Robotics and Embodied AI Lab (REAL). I was a Postdoctoral Researcher with Berkeley Artificial Intelligence Research (BAIR), working with Sergey Levine. His previous and current research has focused on solving sequential decision-making problems for real-world autonomous learning systems (robots). The specific of his research has covered the areas of reinforcement-, continual-, meta-, hierarchical learning, and human-robot collaboration. In his work, Dr. Berseth has published at top venues across the disciplines of robotics, machine learning, and computer animation. Currently, he is teaching a course on robot learning at Université de Montréal and Mila that covers the most recent research on machine learning techniques for creating generalist robots.

To see a more formal biography, click here.

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News

Daniel Geng Articles

Representative Publications

  • Entropy minimization for emergent behaviour

    All living organisms carve out environmental niches within which they can maintain relative predictability amidst the ever-increasing entropy around them [schneider1994, friston2009]. Humans, for example, go to great lengths to shield themselves from surprise --- we band together in millions to build cities with homes, supplying water, food, gas, and electricity to control the deterioration of our bodies and living spaces amidst heat and cold, wind and storm. The need to discover and maintain such surprise-free equilibria has driven great resourcefulness and skill in organisms across very diverse natural habitats. Motivated by this, we ask: could the motive of preserving order amidst chaos guide the automatic acquisition of useful behaviors in artificial agents?