Multi-Modal Robotic World Modeling via Physically Consistent Video Generation and Cross-View Representation Alignment

Abstract

The construction of accurate and coherent world models is a fundamental challenge in autonomous robotics, particularly when agents must operate in unstructured, dynamic environments. This paper introduces a unified framework for multi-modal robotic world modeling that integrates physically consistent video generation with cross-view representation alignment. The proposed architecture leverages generative video models that adhere to physical laws such as conservation of momentum, occlusion reasoning, and object permanence, thereby producing temporally coherent predictions from sparse sensory inputs. Simultaneously, a cross-view representation alignment module maps observations from disparate sensor modalities—including RGB cameras, LiDAR, depth sensors, and radar—into a shared latent space that preserves spatial and temporal consistency. We analyze the structural trade-offs inherent in designing such a system, including the balance between generative fidelity and computational efficiency, the governance of training data diversity, and the robustness of representations under distributional shift. Deployment considerations for edge computing and cloud-in-the-loop architectures are discussed, alongside sustainability metrics related to energy consumption and model carbon footprint. Furthermore, we examine fairness and policy implications arising from biased sensor configurations and uneven representation of environmental conditions. Through a synthesis of recent advances in video diffusion models, neural implicit representations, and contrastive learning, we propose a roadmap for scalable, physically grounded world modeling that can serve as a backbone for downstream planning, navigation, and manipulation tasks. This work contributes a systems-level perspective that bridges computer vision, robotics, and socio-technical infrastructure design.