Embodied Image Compression

• URL: http://arxiv.org/abs/2512.11612v1
• Date: Fri, 12 Dec 2025 14:49:34 GMT
• Title: Embodied Image Compression
• Authors: Chunyi Li, Rui Qing, Jianbo Zhang, Yuan Tian, Xiangyang Zhu, Zicheng Zhang, Xiaohong Liu, Weisi Lin, Guangtao Zhai,
• Abstract summary: This paper introduces, for the first time, the scientific problem of Embodied Image Compression.<n>We establish a standardized benchmark, EmbodiedComp, to facilitate systematic evaluation under ultra-low conditions in a closed-loop setting.<n>We demonstrate that existing Vision-Language-Action models fail to reliably perform even simple manipulation tasks when compressed below the Embodied threshold.
• Score: 105.9462341161654
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Image Compression for Machines (ICM) has emerged as a pivotal research direction in the field of visual data compression. However, with the rapid evolution of machine intelligence, the target of compression has shifted from task-specific virtual models to Embodied agents operating in real-world environments. To address the communication constraints of Embodied AI in multi-agent systems and ensure real-time task execution, this paper introduces, for the first time, the scientific problem of Embodied Image Compression. We establish a standardized benchmark, EmbodiedComp, to facilitate systematic evaluation under ultra-low bitrate conditions in a closed-loop setting. Through extensive empirical studies in both simulated and real-world settings, we demonstrate that existing Vision-Language-Action models (VLAs) fail to reliably perform even simple manipulation tasks when compressed below the Embodied bitrate threshold. We anticipate that EmbodiedComp will catalyze the development of domain-specific compression tailored for Embodied agents , thereby accelerating the Embodied AI deployment in the Real-world.

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