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Events for May 10, 2022

  • PhD Defense - Libby Boroson

    Tue, May 10, 2022 @ 10:00 AM - 12:00 PM

    Thomas Lord Department of Computer Science

    University Calendar

    Title: Robust Loop Closures for Multi-Robot SLAM in Unstructured Environments

    Date/Time: Tuesday, May 10, 2022, 10:00 am - 12:00 pm

    Location: RTH 406 or on Zoom: https://usc.zoom.us/j/94676460358

    Committee: Gaurav Sukhatme (Chair), Nora Ayanian, Stefanos Nikolaidis, Ketan Savla

    Abstract: A key capability for a team of robots operating together in an unknown environment is building and sharing maps. As each robot explores, it must be able to build its own local map and use it for navigation. To take advantage of the benefits of working in a team, the robots should also be able to share and merge those maps. Merging these local maps into a global map requires identification of loop closures, or places where the maps overlap. However, tasks in unstructured environments, such as planetary exploration, are not well-suited to traditional visual loop closure methods like scene or object detection. These tasks may involve robots with unusual sensors, the robots may not observe the same areas, and the environment may not allow for identification of standard visual features, which all make it challenging to identify loop closures. The team may also be heterogeneous, so there may be differences in how and where the robots make their observations.

    This thesis addresses the challenge of identifying robust loop closures in spite of these limitations. It includes several methods that successfully find inter-robot loop closures in challenging unstructured environments, including a method using heterogeneous sensors, a method for robots that view the world from different perspectives, and a method with ranging sensors for scenarios where robots' trajectories do not overlap. It also discusses the Autonomous PUFFER multi-robot SLAM system, a semi-real time system developed for a team of robots operating autonomously in a planetary exploration environment. Finally, it discusses how these techniques provide a framework for future multi-robot mapping in unstructured environments. The maps and systems developed will need to accurately model the environment while also supporting diverse robots and teams.

    Location: Ronald Tutor Hall of Engineering (RTH) - 406

    WebCast Link: https://usc.zoom.us/j/94676460358

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

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  • PhD Defense - James Preiss

    Tue, May 10, 2022 @ 02:00 PM - 04:00 PM

    Thomas Lord Department of Computer Science

    University Calendar

    PhD Candidate: James Preiss

    Title: Characterizing and Improving Robot Learning: A Control-Theoretic Perspective

    May 10, 2022, 2:00-4:00pm PDT

    In-person: RTH 306
    Zoom: https://usc.zoom.us/j/3224457297

    Gaurav S. Sukhatme (chair)
    Nora Ayanian
    Ashutosh Nayyar
    Stefanos Nikolaidis


    The interface between machine learning and control has enabled robots to move outside the laboratory into challenging real-world settings. Deep reinforcement learning can scale empirically to very complex systems, but we do not yet understand precisely when and why it succeeds. Control theory focuses on simpler systems, but delivers interpretability, mathematical understanding, and guarantees. We present projects that combine these strengths.

    In empirical work, we propose a framework for tasks with complex dynamics but known reward functions. We restrict the use of learning to the dynamics modeling stage, and act based on this model using traditional state-space control. We apply this framework to robotic manipulation of deformable objects.

    In theoretical work, we deploy the well-understood linear quadratic regulator (LQR) problem as a test case to "look inside" algorithms and problem structure. First, we investigate how reinforcement learning algorithms depend on properties of the dynamical system by bounding the variance of the REINFORCE policy gradient estimator as a function of the LQR system matrices. Second, we introduce the framework of suboptimal covering numbers to quantify how much a good multi-system policy must change with respect to the dynamics parameters, and bound the covering number for a simple class of LQR systems.

    Location: Ronald Tutor Hall of Engineering (RTH) - 306

    WebCast Link: https://usc.zoom.us/j/3224457297

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

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  • Repeating EventVirtual First-Year Admission Information Session

    Tue, May 10, 2022 @ 04:00 PM - 05:00 PM

    Viterbi School of Engineering Undergraduate Admission

    Workshops & Infosessions

    Our virtual information session is a live presentation from a USC Viterbi admission counselor designed for high school students and their family members to learn more about the USC Viterbi undergraduate experience. Our session will cover an overview of our undergraduate engineering programs, the application process, and more on student life. Guests will be able to ask questions and engage in further discussion toward the end of the session.

    Register Here!

    Audiences: Everyone Is Invited

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    Contact: Viterbi Admission

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