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arXiv · 2601.14921

Vision-Language Models on the Edge for Real-Time Robotic Perception

Sarat Ahmad·Maryam Hafeez·Syed Ali Raza Zaidi
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Abstract

Vision-Language Models (VLMs) enable multimodal reasoning for robotic perception and interaction, but their deployment in real-world systems remains constrained by latency, limited onboard resources, and privacy risks of cloud offloading. Edge intelligence within 6G, particularly Open RAN and Multi-access Edge Computing (MEC), offers a pathway to address these challenges by bringing computation closer to the data source. This work investigates the deployment of VLMs on ORAN/MEC infrastructure using the Unitree G1 humanoid robot as an embodied testbed. We design a WebRTC-based pipeline that streams multimodal data to an edge node and evaluate LLaMA-3.2-11B-Vision-Instruct deployed at the edge versus in the cloud under real-time conditions. Our results show that edge deployment preserves near-cloud accuracy while reducing end-to-end latency by 5\%. We further evaluate Qwen2-VL-2B-Instruct, a compact model optimized for resource-constrained environments, which achieves sub-second responsiveness, cutting latency by more than half but at the cost of accuracy.

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Sarat Ahmad, Maryam Hafeez, Syed Ali Raza Zaidi. 2026-01-21. Vision-Language Models on the Edge for Real-Time Robotic Perception. https://arxiv.org/abs/2601.14921

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