Fatemeh Bahrani — accepted AI-conference papers

2026

AutoFocus-IL: VLM-Based Saliency Maps for Data-Efficient Visual Imitation Learning without Extra Human Annotations

Litian Gong, Fatemeh Bahrani, Yutai Zhou, Amin Banayeeanzade, Jiachen Li, Erdem Bıyık

ICRA 2026poster

We present AutoFocus-IL, a simple yet effective method to improve data efficiency and generalization in visual imitation learning by guiding policies to attend to task-relevant features rather than distractors and spurious correlations. Saliency regularization has emerged as a promising way to achie…

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2025

GABRIL: Gaze-Based Regularization for Mitigating Causal Confusion in Imitation Learning

Amin Banayeeanzade, Fatemeh Bahrani, Yutai Zhou, Erdem Biyik

IROS 2025

Imitation Learning (IL) is a widely adopted approach which enables agents to learn from human expert demonstrations by framing the task as a supervised learning problem. However, IL often suffers from causal confusion, where agents misinterpret spurious correlations as causal relationships, leading

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