LocalNav: Distilling Frontier VLMs and Embodied RL for On-Device Object Goal Navigation
Overview
This paper introduces LocalNav, a method that distills frontier Vision Language Models (VLMs) and applies Embodied Reinforcement Learning from Verifiable Rewards (E-RLVR) to enable low-latency object goal navigation on resource-constrained embedded GPUs like the Jetson Orin. While cloud-based models achieve 41.61% success on the HM3D OVON benchmark, a zero-shot local deployment of Qwen3.5-4B only reaches 25.97%. By distilling reasoning traces and applying E-RLVR, the authors improve local performance to 37.36% SR. Further reducing token generation latency through regularization yields a final success rate of 35.53% with a cumulative 80.21% reduction in overall inference latency when combined with quantization, offering a viable path for local VLM execution on mobile robots.
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Reported quantity · Reported success rate: 41.61 percent · reported trials · Reported zero-shot result; the excerpt does not specify an evaluation protocol.
“on resource-constrained robots. To address this challenge, we present a distillation strategy that aligns a lightweight, 4B- parameter local VLM to our spatial-semantic navigation interface via frontier model traces for edge execution on embedded GPU devices (e.g., Jetson Orin). While cloud models establish a baseline achieving 41.61% Success Rate (SR) on the HM3D OVON benchmark, zero-shot deployment of our proposed”
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- arXiv Robotics — research abstractsSignalLocalNav: Distilling Frontier VLMs and Embodied RL for On-Device Object Goal Navigation
This paper introduces LocalNav, a method that distills frontier Vision Language Models (VLMs) and applies Embodied Reinforcement Learning from Verifiable Rewards (E-RLVR) to enable low-latency object goal navigation on resource-constrained embedded GPUs like the Jetson Orin. While cloud-based models achieve 41.61% success on the HM3D OVON benchmark, a zero-shot local deployment of Qwen3.5-4B only reaches 25.97%. By distilling reasoning traces and applying E-RLVR, the authors improve local performance to 37.36% SR. Further reducing token generation latency through regularization yields a final success rate of 35.53% with a cumulative 80.21% reduction in overall inference latency when combined with quantization, offering a viable path for local VLM execution on mobile robots.
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