Breaking the Vision-Action Shortcut: Latent Interface Training for Generalizable Robotics Foundation Models
Robot foundation models achieve strong in-distribution performance but often degrade under visual distribution shifts. When learning to generate actions from pretrained visual representations, models may exploit task-irrelevant visual cues that correlate with demonstrated actions within the training distribution. Such vision-action shortcuts can undermine generalization when th
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Robot foundation models achieve strong in-distribution performance but often degrade under visual distribution shifts. When learning to generate actions from pretrained visual representations, models may exploit task-irrelevant visual cues that correlate with demonstrated actions within the training distribution. Such vision-action shortcuts can undermine generalization when th
This research addresses a critical challenge in robotics foundation models: their generalization capabilities under visual distribution shifts. By mitigating 'vision-action shortcuts', it aims to make robotic agents more robust and deployable in diverse real-world environments, which is key for practical AI applications.