Embodied Foundation Models at the Edge: A Survey of Deployment Constraints and Mitigation Strategies
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866912973495205888 |
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| author | Grover, Utkarsh Ranjan, Ravi Mao, Mingyang Dong, Trung Tien Praveen, Satvik Wu, Zhenqi Chang, J. Morris Mohsenin, Tinoosh Sheng, Yi Polyzou, Agoritsa Kanjo, Eiman Lin, Xiaomin |
| author_facet | Grover, Utkarsh Ranjan, Ravi Mao, Mingyang Dong, Trung Tien Praveen, Satvik Wu, Zhenqi Chang, J. Morris Mohsenin, Tinoosh Sheng, Yi Polyzou, Agoritsa Kanjo, Eiman Lin, Xiaomin |
| contents | Deploying foundation models in embodied edge systems is fundamentally a systems problem, not just a problem of model compression. Real-time control must operate within strict size, weight, and power constraints, where memory traffic, compute latency, timing variability, and safety margins interact directly. The Deployment Gauntlet organizes these constraints into eight coupled barriers that determine whether embodied foundation models can run reliably in practice. Across representative edge workloads, autoregressive Vision-Language-Action policies are constrained primarily by memory bandwidth, whereas diffusion-based controllers are limited more by compute latency and sustained execution cost. Reliable deployment therefore depends on system-level co-design across memory, scheduling, communication, and model architecture, including decompositions that separate fast control from slower semantic reasoning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_16952 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Embodied Foundation Models at the Edge: A Survey of Deployment Constraints and Mitigation Strategies Grover, Utkarsh Ranjan, Ravi Mao, Mingyang Dong, Trung Tien Praveen, Satvik Wu, Zhenqi Chang, J. Morris Mohsenin, Tinoosh Sheng, Yi Polyzou, Agoritsa Kanjo, Eiman Lin, Xiaomin Robotics Artificial Intelligence Deploying foundation models in embodied edge systems is fundamentally a systems problem, not just a problem of model compression. Real-time control must operate within strict size, weight, and power constraints, where memory traffic, compute latency, timing variability, and safety margins interact directly. The Deployment Gauntlet organizes these constraints into eight coupled barriers that determine whether embodied foundation models can run reliably in practice. Across representative edge workloads, autoregressive Vision-Language-Action policies are constrained primarily by memory bandwidth, whereas diffusion-based controllers are limited more by compute latency and sustained execution cost. Reliable deployment therefore depends on system-level co-design across memory, scheduling, communication, and model architecture, including decompositions that separate fast control from slower semantic reasoning. |
| title | Embodied Foundation Models at the Edge: A Survey of Deployment Constraints and Mitigation Strategies |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2603.16952 |