Cross-Modal World Modeling with HY-Himmel: Unifying Video, Text, and Sensor Streams for Embodied AI
Keywords:
cross-modal world modeling, embodied AI, multimodal learning, video understanding, sensor fusion, hierarchical encoding, governance, sustainabilityAbstract
The emergence of embodied artificial intelligence demands unified world models that can seamlessly integrate heterogeneous sensory modalities including vision, natural language, and structured sensor streams. This paper presents a comprehensive analysis of HY-Himmel, a hierarchical interleaved multi-stream motion encoding framework designed for long video understanding and extended to cross-modal world modeling. The architecture addresses fundamental challenges in aligning temporally asynchronous data from video, text instructions, and sensor readings through a nested encoding hierarchy that preserves both fine-grained temporal dynamics and high-level semantic abstractions. We examine the structural trade-offs between model expressivity and computational tractability, the infrastructural requirements for deploying such systems in real-world robotic and autonomous environments, and the governance implications of unifying multi-modal data under a single representational framework. Sustainability considerations are discussed in the context of energy-efficient training and inference, while robustness and fairness are evaluated with respect to domain shift and representation bias. Policy implications arising from the use of cross-modal models in critical infrastructure and public services are also addressed. By situating HY-Himmel within the broader landscape of large-scale multimodal foundation models, this paper offers a systematic exploration of the architectural, operational, and societal dimensions of next-generation embodied AI systems.
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