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Events for December 05, 2024
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PhD Dissertation Defense - Jingyao Ren
Thu, Dec 05, 2024 @ 11:00 AM - 01:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Advancements in Understanding the Empirical Hardness of the Multi-Agent Pathfinding Problem
Date: December 5TH, 11:00 AM to 1:00 PM
Location: EEB 110
Committee: T.K. Satish Kumar (Chair), Stefanos Nikolaidis, Feifei Qian, Sven Koenig
Abstract: Multi-Agent Path Finding~(MAPF) involves finding collision-free paths for agents in shared environments and is crucial for applications like automated warehouses and swarm control. While solving MAPF optimally is NP-hard, many real-world instances are solvable efficiently, though factors affecting instance hardness remain unclear. This dissertation explores MAPF empirical hardness, addressing what makes instances hard, how to predict hardness, and ways to generate challenging instances. Key contributions include formalizing empirical hardness research in MAPF, introducing the MAPFAST algorithm selection framework, identifying map connectivity as a critical factor, and demonstrating methods to generate instances with varying hardness.Location: Hughes Aircraft Electrical Engineering Center (EEB) - 110
Audiences: Everyone Is Invited
Contact: Jingyao Ren
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
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PhD Dissertation Defense - Kyle Reing
Thu, Dec 05, 2024 @ 12:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Tractable Information Decompositions
Date and Time: Thursday, December 5th - 12:00p - 2:00p
Location: SGM 226
Committee Members: Aram Galstyan, Greg Ver Steeg, Laurent Itti, Aiichiro Nakano, Antonio Ortega
Abstract: The study of Information Decomposition attempts to represent the functional relationships of a system in a way that makes them transparent and interpretable. While these theoretically grounded measures excel in their descriptive capabilities, they lack computationally feasible implementations, rendering them unusable for practical application and discovery. This dissertation details a collection of work aimed towards computationally tractable approaches to information decomposition which are still theoretically sound. A number of new algorithmic approaches are proposed, studied, and implemented, with numerous applications of these methods to problems in neural network interpretability.Location: Seeley G. Mudd Building (SGM) - 226
Audiences: Everyone Is Invited
Contact: Kyle Reing
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Is Data All You Need?: Large Robot Action Models and Good Old Fashioned Engineering
Thu, Dec 05, 2024 @ 03:00 PM - 05:15 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ken Goldberg, Ph.D., William S. Floyd Distinguished Chair of Engineering - UC Berkeley
Talk Title: Is Data All You Need?: Large Robot Action Models and Good Old Fashioned Engineering
Abstract: Enthusiasm has been skyrocketing for humanoids based on recent advances in "end-to-end" large robot action models. Initial results are promising, and several collaborative efforts are underway to collect the needed demonstration data. But is data really all you need?
Although end-to-end Large Vision, Language, Action (VLA) Models have potential to generalize and reliably solve all problems in robotics, initial results have been mixed[1]. It seems likely that the size of the VLA state space and dearth of available demonstration data, combined with challenges in getting models to generalize beyond the training distribution and the inherent challenges in interpreting and debugging large models, will make it difficult for pure end-to-end systems to provide the kind of robot performance that investors expect in the near future.
In this presentation, I share my concerns about current trends in robotics, including task definition, data collection, and experimental evaluation. I propose that to reach expected performance levels, we will need "Good Old Fashioned Engineering (GOFE)" – modularity, algorithms, and metrics. I'll present MANIP[2], a modular systems architecture that can integrate learning with well-established procedural algorithmic primitives such as Inverse Kinematics, Kalman Filters, RANSAC outlier rejection, PID modules, etc. I’ll show how we are using MANIP to improve performance on robot manipulation tasks such as grasping, cable untangling, surgical suturing, motion planning, and bagging, and propose open directions for research.
Presented at:
>Stanford Robotics Seminar, 19 April, 2024 4-min video clip
>Berkeley AI Research (BAIR) Seminar, 24 April, 2024
>IEEE ICRA Workshop, Yokohama Japan, 16 May 2024
>Berkeley Sky Lab Retreat Keynote, Santa Cruz, 29 May 2024
>Amazon Lab 126, Sunnyvale, CA, 18 June 2024
>Apple Park, Cupertino, CA, 24 July 2024
>Toyota Research Lab, San Jose, CA, 31 July 2024
>ICRA@40 Keynote, Rotterdam, 23 Sept 2024
>WAFR Keynote, Chicago, 7 Oct 2024
>Univ of Southern California (USC) Computer Science Distinguished Lecture Seminar, 5 Dec 2024
[1] Nishanth J. Kumar. Will Scaling Solve Robotics? The idea of solving the biggest robotics challenges by training large models is sparking debate. IEEE Spectrum. 28 May 2024.
[2] MANIP: A Modular Architecture for iNtegrating Iteractive Perception into Long-Horizon Robot Manipulation Systems. Justin Yu*, Tara Sadjadpour*, Abby O’Neill, Mehdi Khfifi, Lawrence Yunliang Chen, Richard Cheng, Ashwin Balakrishna, Thomas Kollar, Ken Goldberg. IEEE/RSJ International Conference on Robots and Systems (IROS), Abhu Dhabi, UAE. Oct 2024. Paper
**LOCATION CHANGE**
GINSBURG COMPUTATION HALL (GCS)
AUDITORIUM
This lecture satisfies requirements for CSCI 591: Research Colloquium.
This lecture will be presented as a HYBRID presentation, but will not be recorded.
Zoom details below: https://usc.zoom.us/j/94205149719?pwd=LjETcnHLvCzyDbB6LjjxfknZaab3Dm.1
Meeting ID: 942 0514 9719 | Passcode: 400232
Biography: Ken Goldberg is William S. Floyd Distinguished Chair of Engineering at UC Berkeley and Chief Scientist of Ambi Robotics and Jacobi Robotics. Ken leads research in robotics and automation: grasping, manipulation, and learning for applications in warehouses, industry, homes, agriculture, and robot-assisted surgery. He is Professor of IEOR with appointments in EECS and Art Practice. Ken is Chair of the Berkeley AI Research (BAIR) Steering Committee (60 faculty) and is co-founder and Editor-in-Chief emeritus of the IEEE Transactions on Automation Science and Engineering (T-ASE). He has published ten US patents, over 400 refereed papers, and presented over 600 invited lectures to academic and corporate audiences.
http://goldberg.berkeley.edu
Host: USC Thomas Lord Department of Computer Science
More Info: https://forms.gle/w1r6Yo3se3WU8Bou7
Webcast: https://usc.zoom.us/j/94205149719?pwd=LjETcnHLvCzyDbB6LjjxfknZaab3Dm.1Location: Ginsburg Hall (GCS) - Auditorium
WebCast Link: https://usc.zoom.us/j/94205149719?pwd=LjETcnHLvCzyDbB6LjjxfknZaab3Dm.1
Audiences: Everyone Is Invited
Contact: USC Thomas Lord Department of Computer Science
Event Link: https://forms.gle/w1r6Yo3se3WU8Bou7
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
