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  • PhD Defense - Ryan Julian

    Tue, May 11, 2021 @ 04:00 PM - 05:30 PM

    Thomas Lord Department of Computer Science

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    PhD Candidate: Ryan Julian

    Committee: Prof. Gaurav Sukhatme (chair), Prof. Satyandra Gupta, Prof. Heather Culbertson, Prof. Joseph Lim, Prof. Stefanos Nikolaidis, Dr. Karol Hausman

    Title: Algorithms and Systems for Continual Robot Learning

    The last decade has seen the rapid evolution of machine learning (ML) from an academic curiosity to an essential capability of modern computing systems, and with it has come an explosion of ML applications which help humans in the real world every day. Whether machine learning can affect such an evolution in the nearby field of robotics--concerned with automating tasks in the physical, rather than the digital world--remains to be seen. Though the last decade has seen an explosion of new methods, remarkably few of these exhibit properties which make them good candidates for building an efficient, continual, multi-task robot skill learning system. This of course begs the question: given a novel method, how should we assess its suitability for building a continually-learning multi-task robot? In other words, how can we systematically assure progress towards new real world capabilities, and avoid getting trapped, running in circles studying novel trivialities? I will argue that three elements--benchmarks, baselines, and novel methods--together form the three-legged stool of research in artificial intelligence. Robotics can only make progress towards the goal of real-world, general-purpose, continually-learning robots by periodically advancing each of these legs, creating a virtuous cycle of new challenges, new systems, new solutions, and ultimately new knowledge. We will follow one such cycle for a small slice of the field, namely continual learning for robotic manipulation, visiting each step to illustrate its effectiveness by example.

    Zoom Info:
    Topic: PhD Defense - Ryan Julian
    Time: May 11, 2021 04:00 PM Pacific Time (US and Canada)

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    WebCast Link: https://usc.zoom.us/j/94821577914?pwd=NW14YW5WWjdPcXVna3grcjZqQnFuQT09

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

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