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Events for June 01, 2022
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PhD Thesis Proposal - Isabel Rayas
Wed, Jun 01, 2022 @ 01:30 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Isabel Rayas
Title: Advancing robot autonomy for long-horizon tasks
Committee:
Prof. Gaurav Sukhatme (chair)
Prof. Stefanos Nikolaidis
Prof. Dave Caron
Prof. Heather Culbertson
Prof. S.K. Gupta
Abstract:
Autonomy is essential for unstructured, long-horizon robotic tasks. Three aspects that help enable autonomy include allowing high-level goal descriptions in the task specification; reducing human intervention required to complete the task; and actively using information gained so far or about the problem in order to make a decision at each step in the task. In this talk, I will discuss how we can use techniques in motion planning to plan efficient motions for long-horizon, sequential tasks, and to learn how to represent motion constraints from demonstrations. Additionally, I will describe recent work and propose several projects using techniques in informative path planning to allow one or more autonomous robots to explore an environment while gathering information useful to the scientists that deployed them.
Zoom info:
Time: Jun 1, 2022 01:30 PM Pacific Time (US and Canada)
https://usc.zoom.us/j/91309840836?pwd=WXpsYXVuak1VVHlYcnYyYk9mNmZKZz09
Location: Ronald Tutor Hall of Engineering (RTH) - 406
WebCast Link: https://usc.zoom.us/j/91309840836?pwd=WXpsYXVuak1VVHlYcnYyYk9mNmZKZz09
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Thesis Proposal - Yilei Zeng
Wed, Jun 01, 2022 @ 06:30 PM - 07:30 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Yilei Zeng
Title: "Learning Social Sequential Decision Making in Online Games"
Date and Time: 06/01 6:30pm
Committee:
Emilio Ferrara(Chair), Aiichiro Nakano(CS tenured), Stefanos Nikolaidis(CS tenure track), Dimitri Williams(Annenberg tenured), Michael Zyda (CS)
Abstract:
As the most significant entertainment industry by far, online games provide many of the most immersive experiences and are perceived as entrance points to the Metaverse. As the virtual worlds become more social and personalized, the need for human-centered AI to understand and model how humans make decisions in games grows. This thesis proposal introduces human-centered recommender systems in games that expand on three scales. We present social scenarios in microscale teams, mesoscale communities, and macroscale crowds. We also show the efficacies of small, heterogeneous, and multimodal data. The applications on the three scales are generalizable toward broader shopping, social, and content recommendations.
WebCast Link: https://usc.zoom.us/j/92485472421?pwd=cWxqQlIxa2Q3bHEvbkRiUnNEZFE2UT09
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
