Action-Aware Video Prediction for Anticipatory Robot Perception and Decision-Making
Keywords:
action-aware video prediction; anticipatory perception; world models; robot decision-making; AI governance; sociotechnical systemsAbstract
Anticipatory perception is becoming a foundational capability for autonomous systems that must operate in dynamic physical and social environments. This paper presents a system-level examination of action-aware video prediction as a mechanism for enabling robots to forecast the visual consequences of their own actions and the actions of nearby agents. We analyze the architectural tensions among generative prediction fidelity, temporal consistency, memory capacity, and control latency. Rather than treating video prediction as an isolated computer vision task, the paper examines its integration into closed-loop robot decision-making, planning, and simulation-based foresight. The discussion covers hierarchical world models, action-conditional generative models, internal memory structures, and the coupling of predictive perception with policy learning. We further examine deployment infrastructure, sustainability, robustness under distribution shift, fairness across operational contexts, and the governance arrangements required for accountable anticipatory systems. The paper argues that action-aware video prediction should be understood as a socio-technical infrastructure component rather than merely a standalone model. Its design requires balancing technical performance with safety, transparency, and institutional oversight. The analysis offers a forward-looking perspective on how anticipatory robot perception can support more capable yet trustworthy autonomous systems.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
