VL-Nav: Real-time Vision-Language Navigation with Spatial Reasoning

• URL: http://arxiv.org/abs/2502.00931v2
• Date: Mon, 10 Feb 2025 06:05:38 GMT
• Title: VL-Nav: Real-time Vision-Language Navigation with Spatial Reasoning
• Authors: Yi Du, Taimeng Fu, Zhuoqun Chen, Bowen Li, Shaoshu Su, Zhipeng Zhao, Chen Wang,
• Abstract summary: We present a novel vision-language navigation (VL-Nav) system that integrates efficient spatial reasoning on low-power robots. Unlike prior methods that rely on a single image-level feature similarity to guide a robot, our method integrates pixel-wise vision-language features with curiosity-driven exploration. VL-Nav achieves an overall success rate of 86.3%, outperforming previous methods by 44.15%.
• Score: 11.140494493881075
• License:
• Abstract: Vision-language navigation in unknown environments is crucial for mobile robots. In scenarios such as household assistance and rescue, mobile robots need to understand a human command, such as "find a person wearing black". We present a novel vision-language navigation (VL-Nav) system that integrates efficient spatial reasoning on low-power robots. Unlike prior methods that rely on a single image-level feature similarity to guide a robot, our method integrates pixel-wise vision-language features with curiosity-driven exploration. This approach enables robust navigation to human-instructed instances across diverse environments. We deploy VL-Nav on a four-wheel mobile robot and evaluate its performance through comprehensive navigation tasks in both indoor and outdoor environments, spanning different scales and semantic complexities. Remarkably, VL-Nav operates at a real-time frequency of 30 Hz with a Jetson Orin NX, highlighting its ability to conduct efficient vision-language navigation. Results show that VL-Nav achieves an overall success rate of 86.3%, outperforming previous methods by 44.15%.

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