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<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonstate.zoom.us%2Fj%2F98684050301%3Fpwd%3DZzhianQxUFBPUmdYVWJKOFhaVURCQT09&data=05%7C02%7Cai%40engr.orst.edu%7C446375e851e34e7b46db08dc1d2e3523%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638417331488687294%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=x%2FaSBG%2B3TLR%2BI%2BR%2BirdmDxSN%2Bar%2F%2Ba3ZF12YhKjrrm8%3D&reserved=0> 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<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineering.oregonstate.edu%2FEECS%2Fresearch%2FAI&data=05%7C02%7Cai%40engr.orst.edu%7C446375e851e34e7b46db08dc1d2e3523%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638417331488687294%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=s1uZ03%2FcSToSGNptl8gm3%2F4Tan1NeJvpfKOTwT96sYE%3D&reserved=0> Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged