I am a Master's student at the University of Alberta under the supervision of Dr. Jun Jin. My research interests lie at the intersection of robotics and machine learning, particularly I am interested in developing embodied continual learners. My current focus is on finding better ways for machines/robots to represent their interactions with the environment, enhancing their learning process.
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University of AlbertaDepartment of Electrical and Computer Engineering
M.Sc. StudentMay. 2025 - present -
Ferdowsi University of MashhadB.Sc. in Computer EngineeringSep. 2019 - Feb. 2024
News
Selected Publications (view all )
Retrospective and Structurally Informed Exploration via Cross-task Successor Feature Similarity
Arya Ebrahimi, Jun Jin
The Exploration in AI Today Workshop at ICML 2025
We introduce Cross-task Successor Feature Similarity Exploration (C-SFSE), a novel intrinsic reward mechanism that leverages retrospective similarities in task-conditioned successor features to prioritize exploration of semantically meaningful states. C-SFSE constructs a cross-task similarity signal from previously learned policies, identifying regions, such as bottlenecks or reusable subgoals, that consistently support goal-directed behavior. This enables the agent to focus its exploration on state space areas that are not only novel but informative across tasks.
Retrospective and Structurally Informed Exploration via Cross-task Successor Feature Similarity
Arya Ebrahimi, Jun Jin
The Exploration in AI Today Workshop at ICML 2025
We introduce Cross-task Successor Feature Similarity Exploration (C-SFSE), a novel intrinsic reward mechanism that leverages retrospective similarities in task-conditioned successor features to prioritize exploration of semantically meaningful states. C-SFSE constructs a cross-task similarity signal from previously learned policies, identifying regions, such as bottlenecks or reusable subgoals, that consistently support goal-directed behavior. This enables the agent to focus its exploration on state space areas that are not only novel but informative across tasks.