UniMPA: A Unified Memory-Prediction-Action Model via Action-Grounded Transition Modeling

• URL: http://arxiv.org/abs/2609.11875v1
• Date: Thu, 10 Sep 2026 17:45:03 GMT
• Title: UniMPA: A Unified Memory-Prediction-Action Model via Action-Grounded Transition Modeling
• Abstract summary: Recent advances in Vision-Language-Action models have improved robotic manipulation, yet observation-to-action learning remains limited by a fundamental transition realizability gap.<n>A visually plausible predicted future observation does not necessarily correspond to a physically realizable transition.<n>A historically executable action pattern may not necessarily realize the intended transition in the current scene and therefore requires context-aware adaptation.
• Score: 73.35395707747456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in Vision-Language-Action (VLA) models have improved robotic manipulation, yet observation-to-action learning remains limited by a fundamental transition realizability gap, manifested in three tightly coupled problems: (i) Transition ambiguity. Visually similar current observations may correspond to different manipulation phases and imply different subsequent transitions. (ii) Prediction--execution mismatch. A visually plausible predicted future observation does not necessarily correspond to a physically realizable transition. (iii) Experience--realization mismatch. A historically executable action pattern may not necessarily realize the intended transition in the current scene and therefore requires context-aware adaptation. Accordingly, we propose UniMPA, a Unified Memory-Prediction-Action model that addresses these problems through a shared action-grounded transition interface. (i) UniMPA introduces Persistent-Selective Future Prediction to resolve transition ambiguity by modeling the intended future state evolution. A persistent latent stream continuously tracks task-level progress, while a transition-critical pixel stream selectively resolves fine-grained interaction changes through memory-grounded prediction. (ii) To assess the physical executability of the anticipated transition, the predicted transition queries a temporal Visual-Action Memory Bank. The bank retrieves historically realized visual-action experience, grounding future prediction in executable evidence. (iii) To adapt executable experience to the current scene, an Action-Visual Memory Bank retrieves visually grounded action prototypes from historical action evolution. Prototype-Biased Flow then shifts the flow source toward a historically supported action manifold for context-aware refinement.

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