Selected Publications
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Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill
Diversity
Lin Guan*, Sarath Sreedharan* (equal contribution), Subbarao Kambhampati
ICML 2022
(also received the
Best Paper Award
at PRL@ICAPS 2022 and accepted to RLDM 2022)
Symbolic knowledge is important for solving long-horizon task and motion planning tasks.
But a key resistance to leveraging easily available human symbolic knowledge has been that it might be inexact.
In this work, we present a framework to quantify the relationship between the true task model and an inexact STRIPS model, and
introduce a novel approach using landmarks and a diversity objective to make up for potential errors in the symbolic knowledge.
paper
 
website
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Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset
Ruohan Zhang, Calen Walshe, Zhuode Liu, Lin Guan, Karl S. Muller, Jake A. Whritner, Luxin
Zhang, Mary M Hayhoe, Dana H Ballard
AAAI 2020
paper
We provide a large-scale, high-quality dataset of human actions with simultaneously
recorded eye movements (i.e., gaze info) while humans play Atari video games.
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