16 citations · 17 across the 7 of their papers we have counts for
3 papers · 1 filter
AutoFocus-IL: VLM-based Saliency Maps for Data-Efficient Visual Imitation Learning without Extra Human Annotations
Litian Gong, Fatemeh Bahrani, Yutai Zhou +3
AutoFocus-IL is a simple yet effective method to improve data efficiency and generalization in visual imitation learning by guiding policies to attend to task-relevant features rat…
GABRIL: Gaze-Based Regularization for Mitigating Causal Confusion in Imitation Learning
Amin Banayeeanzade, Fatemeh Bahrani, Yutai Zhou +1
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, I…
CLAM: Continuous Latent Action Models for Robot Learning from Unlabeled Demonstrations
Anthony Liang, Pavel Czempin, Matthew M. Hong +4
Learning robot control policies from demonstrations typically requires action-labeled expert data, which is expensive to collect through teleoperation. We study a more practical se…