Physically Grounded Multimodal World Models for Embodied Robot Interaction via 3D Representation Alignment
Keywords:
physically grounded world models; multimodal alignment; 3D representation learning; embodied AI; robot interaction; simulation-to-real transfer; governanceAbstract
The deployment of autonomous robots in unstructured, human-centric environments demands perception-action systems that are simultaneously multimodal, physically coherent, and spatially precise. While recent advances in foundation models have produced powerful language-vision representations and high-fidelity 3D scene reconstructions, a fundamental gap persists between passive recognition and dynamic physical interaction. This paper presents a system-level investigation into physically grounded multimodal world models that integrate 3D representation alignment as a central scaffold for embodied robot control. We examine the architectural trade-offs involved in constructing world models that merge visual, linguistic, proprioceptive, and physical simulation modalities within unified 3D coordinate frames. The discussion focuses on how explicit 3D geometric alignment across sensing streams enables more robust sim-to-real transfer, facilitates long-horizon planning, and supports physical reasoning under uncertainty. We analyze infrastructure and deployment challenges, including latency constraints, computational sustainability, and the governance of data that couples real-world physics with learned priors. Furthermore, we address issues of fairness, bias, and policy implications arising from physically grounded systems that learn behavioral policies from human demonstrations and synthetic experience. By synthesizing insights from neural rendering, multimodal learning, robotics, and AI governance, the paper offers a forward-looking perspective on building physically intelligent robots that are not only capable but also accountable in their interactions with the world.
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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.
