Gaze regularization aligns VLA attention with human visual patterns via KL divergence on patch distributions, yielding 4-12% gains on manipulation benchmarks.
Enhancing reusability of learned skills for robot manipulation via gaze information and motion bottlenecks.IEEE Robotics and Automation Let- ters, 10(10):10737–10744
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Gaze-Regularized Vision-Language-Action Models for Robotic Manipulation
Gaze regularization aligns VLA attention with human visual patterns via KL divergence on patch distributions, yielding 4-12% gains on manipulation benchmarks.