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SUNMONTUEWEDTHUFRISAT
Events for the 1st week of February
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Computer Science General Faculty Meeting
Wed, Jan 31, 2024 @ 12:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
Receptions & Special Events
Bi-Weekly regular faculty meeting for invited full-time Computer Science faculty only. Event details emailed directly to attendees.
Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Invited Faculty Only
Contact: Assistant to CS Chair
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CCI, AAI, and MHI Joint Seminar Series - Baxi Chong (Georgia Tech): Gait coding scheme for multi-legged robots
Wed, Jan 31, 2024 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Baxi Chong, Georgia Tech
Talk Title: Gait coding scheme for multi-legged robots
Abstract: While the transport of matter by wheeled vehicles or legged robots can be guaranteed in engineered landscapes like roads or rails, locomotion prediction in complex environments like collapsed buildings or crop fields remains challenging. Inspired by principles of information transmission which allow signals to be reliably transmitted over noisy channels, we develop a “matter transport" framework demonstrating that non-inertial locomotion can be provably generated over “noisy" rugose landscapes (heterogeneities on the scale of locomotor dimensions). Experiments confirm that sufficient spatial redundancy in the form of seriallyconnected legged robots leads to reliable transport on such terrain without requiring sensing and control.
Despite robustness, locomotors with excessively redundant legs are often practically unfavored because of limited efficiency and applicability. Analogous to signal transmission, we further improve locomotion efficiency by properly coordinating (coding) the redundant legs. The challenges of such coding partially lie on the high dimensionality associated with the additional legs and the emergent importance of inter-leg centralized coordination. Specifically, we need a top-down approach to analyze the central coordination among the additional legs, and further design how it should adapt to different environments. We use geometric mechanics, a mathematical framework for studying locomotion in various systems, for motion planning in multi-legged robots operating in complex environments. As a result, open-loop operation on multi-legged robots achieves remarkable performance on terrains with different types and levels of complexity. Additionally, analogies from communication theory coupled to advances in coding for error detection/correction further improve the locomotion efficiency and robustness via centralized adaptation (using simple contact sensors to estimate environmental uncertainty). This research contributes to the field of legged robot locomotion, providing new possibilities for designing effective and adaptable robots for challenging environments.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Zoom Link: https://usc.zoom.us/j/98624281836?pwd=ajJSWGRvbkRpUVgvRC9nOXd5K29TZz09
Meeting ID: 986 2428 1836 Passcode: CPS24
Biography: Dr. Baxi Chong is a postdoctoral fellow at the CRAB (Complex Rheology And Biomechanics) Lab in the School of Physics at Georgia Tech. His research focuses on locomotion, aiming to diversify robot morphology with reference to evolutionary biology. Dr. Chong has contributed to high-impact journals and conferences such as Science, PNAS, IJRR, and RSS. Additionally, he actively serves as a reviewer for robotics conferences and journals, including ICRA, IROS, IJRR, and TRO. Dr. Chong obtained his Ph.D. from Georgia Tech and his Bachelor of Engineering from the University of Hong Kong.
Host: Pierluigi Nuzzo and Feifei Qian
More Info: https://usc.zoom.us/j/98624281836?pwd=ajJSWGRvbkRpUVgvRC9nOXd5K29TZz09
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: CS Events
Event Link: https://usc.zoom.us/j/98624281836?pwd=ajJSWGRvbkRpUVgvRC9nOXd5K29TZz09
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PhD Thesis Proposal - Matthew Ferland
Thu, Feb 01, 2024 @ 12:30 AM - 02:00 PM
Thomas Lord Department of Computer Science
University Calendar
Event: PhD Thesis Proposal (Matthew Ferland)
Committee: Shanghua Teng, David Kempe, Jiapeng Zhang, Shaddin Dughmi, and Larry Goldstein
Date: February 1, 2024, 12:30pm – 2:00pm
Title: Exploring the complexity landscape of combinatorial games
Abstract: People have been playing games since before written history, and many of the earliest games were combinatorial games, that is to say, games of perfect information and no chance. This type of game is still widely played today, and many popular games of this type, such as Chess and Go, are some of the most studied games of all time. This proposed work resolves around a game-independent systemic study of these games, involving computational properties involving evaluating the mathematical analysis tools, such as sprague-grundy values and switches, as well identifying what can be determined about these games under simple oracle models.
Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: CS Events
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PhD Defense - KR Zentner
Thu, Feb 01, 2024 @ 02:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Leveraging Cross-Task Transfer in Sequential Decision Problems: Scalable Reinforcement Learning for Robotics
Defense Committee: Gaurav Sukhatme (chair), Heather Culbertson, Stefanos Nikolaidis, Laurent Itti, Bhaskar Krishnamachari
Date: Feb 1, 2024, 2 p.m. - 4 p.m. - RTH 217
Abstract: The past few years have seen an explosion of interest in using machine learning to make robots capable of learning a diverse set of tasks. Potentially, these robots could operate in close proximity to humans, assisting humans with a wide variety of needs and being instructed to perform new tasks as needed. However, these robots generally use Reinforcement Learning to learn detailed sub-second interactions, but consequently require large amounts of data for each task. In this thesis we explore how Reinforcement Learning can be combined with Transfer Learning to re-use data across tasks. We begin by reviewing the state of Multi-Task and Meta RL and describe the motivations for using Transfer Learning. Then, we describe a basic framework for using Transfer Learning to efficiently learn multiple tasks, and show how it requires predicting how effectively transfer can be performed across tasks. Next, we present a simple rule, based in information theory, for predicting the effectiveness of Cross-Task Transfer, which we call the "Transfer Cost Rule." We discuss the theoretical implications of that rule, and show various quantitative evaluations of it. Then, we show two directions of work making use of our insights to perform efficient Transfer Reinforcement Learning. The first of these directions uses Cross-Task Co-Learning and Plan Conditioned Behavioral Cloning to share skill representations produced by a Large Language Model, and is able to learn many tasks from a single demonstration each in a simulated environment. The second of these directions uses Two-Phase KL Penalization to enforce a (potentially off-policy) trust region. These advances in Transfer RL may enable robots to be used in a wider range of applications, such as in the home or office. The insight provided by the Transfer Cost Rule may also be relevant to a wide audience of Reinforcement Learning practitioners, since it provides a practical and theoretically grounded explanation for the performance of Deep Reinforcement Learning algorithms.
Zoom link: https://usc.zoom.us/j/96965616504?pwd=QngwQTJsTXJkbXJJNU9hRVV2Mk1DQT09Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: CS Events
Event Link: https://usc.zoom.us/j/96965616504?pwd=QngwQTJsTXJkbXJJNU9hRVV2Mk1DQT09
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PhD Thesis Proposal - Hsien-Te Kao
Fri, Feb 02, 2024 @ 01:00 PM - 02:30 PM
Thomas Lord Department of Computer Science
University Calendar
Committee: Emilio Ferrara (Chair), Kristina Lerman, Phebe Vayanos, Souti Chattopadhyay, Ruishan Liu
Date and Time: Friday, February 2, 2024, 1:00 PM - 2:30 PM PST - RTH 115
Title: Cold Start Prediction in Personalized mHealth
Abstract: Mobile health has brought fundamental changes to the healthcare industry, offering new hope in addressing growing healthcare expenditures, opportunity costs, and labor shortages. Machine learning is driving mobile health towards decentralized healthcare by automating health monitoring, diagnosis, and treatment. Personalized mobile health systems are a key component in advancing patient-centric healthcare, but these systems remain unfeasible outside of hospital settings because personal health data is largely inaccessible, uncollectible, and regulated. In this proposal, we introduce a personalized mobile health system to predict individual health status without user context through a set of mobile, wearable, and ubiquitous technologies. The model leverages collaborative filtering to replace missing user context with learned similar group characteristics, where user similarity is captured through multiple dimensions of cognitive appraisal based on a combination of psychology theories. The system eliminates user dependence through passive feedback that satisfies real-world constraints. Our preliminary results demonstrate a proof-of-concept system.Location: Ronald Tutor Hall of Engineering (RTH) - 115
Audiences: Everyone Is Invited
Contact: CS Events
