Problem
Research questions and friction points this paper is trying to address.
Vision-Language-Action (VLA)
Predictive Supervision
Representation Learning
Distribution Shift
Future Prediction
Where Predictive Supervision Goes Shapes What VLA Policies Learn
🤖 AI Summary
This study addresses the unclear mechanisms by which predictive supervision influences visual representation learning in vision-language-action (VLA) policies and enhances control performance under distribution shifts. By reframing future prediction as a representation learning design problem, this work systematically analyzes how different predictive interfaces shape spatial, dynamic, and action-related information through controlled experiments. The proposed approach employs goal-matching construction and multi-horizon prediction techniques, validated via distribution shift evaluations in both simulated and physical environments. The findings reveal the specific pathways through which prediction errors propagate into the visual stream, demonstrating that supervisory signals must be effectively encoded into action-relevant representations. Furthermore, more direct and scene-aligned future supervision significantly improves the robustness and control performance of VLA policies under distribution shifts.
📝 Abstract
Future prediction is increasingly used to improve vision-language-action (VLA) policies, based on the premise that anticipating scene evolution encourages representations useful for control. However, forecast quality alone does not establish that a policy has learned a better representation for action. This distinction matters under distribution shift, where successful control depends on preserving spatial state and likely scene change beyond familiar configurations. We study what determines whether predictive supervision improves the visual representation used by a VLA policy. Through controlled comparisons with matched target constructions, prediction horizons, and training conditions, we find that different prediction interfaces produce markedly different forecasts and visual representations, including in the spatial, dynamics, and action information that transfers beyond familiar scenes. We trace these differences to how predictive errors shape the policy's visual stream. Consistent with this controlled finding, VLA policies trained with more direct, scene-matched future supervision show stronger robustness under simulated and physical distribution shifts. Together, our results frame future prediction as a representation-learning design problem whose value for control depends on whether its supervision reaches the representations through which the policy acts.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Vision-Language-Action (VLA)
Predictive Supervision
Representation Learning
Distribution Shift
Future Prediction
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