Dear all,
Our next AI seminar is scheduled to be on January 26th, 2-3 PM. It will be followed by a 30-minute Q&A session with the graduate students.
Location: KEC 1001
Zoom link: https://oregonstate.zoom.us/j/98684050301?pwd=ZzhianQxUFBPUmdYVWJKOFhaVURCQT09
Value-Based Abstractions for Planning
Amy Zhang
Assistant Professor
Chandra Family Department of Electrical and Computer Engineering
University of Texas at Austin
Abstract:
As the field of robotics continues to advance, the integration of efficient planning algorithms with powerful representation learning becomes crucial for enabling robots to perform complex manipulation tasks. We address key challenges
in planning, reward learning, and representation learning through the objective of learning value-based abstractions. We explore this idea via goal-conditioned reinforcement learning, action-free pre-training, and with language. By leveraging self-supervised
reinforcement learning and efficient planning algorithms, these approaches collectively contribute to the advancement of robotic systems capable of learning and adapting to diverse tasks in real-world environments.
Speaker Bio:
I am an assistant professor at UT Austin in the Chandra Family Department of Electrical and Computer Engineering. My work focuses on improving generalization in reinforcement learning through bridging theory and practice in learning
and utilizing structure in real world problems. Previously I was a research scientist at Meta AI - FAIR and a postdoctoral fellow at UC Berkeley. I obtained my PhD from McGill University and the Mila Institute, and also previously obtained an M.Eng. in EECS
and dual B.Sci. degrees in Mathematics and EECS from MIT.
Please watch this space for future AI Seminars :
https://engineering.oregonstate.edu/EECS/research/AI
Rajesh Mangannavar,
Graduate Student
Oregon State University
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AI Seminar Important Reminders:
-> For graduate students in the AI program, attendance is strongly encouraged