Fast-SmartWay: Panoramic-Free End-to-End Zero-Shot Vision-and-Language Navigation

• URL: http://arxiv.org/abs/2511.00933v1
• Date: Sun, 02 Nov 2025 13:21:54 GMT
• Title: Fast-SmartWay: Panoramic-Free End-to-End Zero-Shot Vision-and-Language Navigation
• Authors: Xiangyu Shi, Zerui Li, Yanyuan Qiao, Qi Wu,
• Abstract summary: Fast-SmartWay is an end-to-end zero-shot VLN-CE framework that eliminates the need for panoramic views and waypoint predictors.<n>Our approach uses only three frontal RGB-D images combined with natural language instructions, enabling MLLMs to directly predict actions.
• Score: 16.632191523127865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in Vision-and-Language Navigation in Continuous Environments (VLN-CE) have leveraged multimodal large language models (MLLMs) to achieve zero-shot navigation. However, existing methods often rely on panoramic observations and two-stage pipelines involving waypoint predictors, which introduce significant latency and limit real-world applicability. In this work, we propose Fast-SmartWay, an end-to-end zero-shot VLN-CE framework that eliminates the need for panoramic views and waypoint predictors. Our approach uses only three frontal RGB-D images combined with natural language instructions, enabling MLLMs to directly predict actions. To enhance decision robustness, we introduce an Uncertainty-Aware Reasoning module that integrates (i) a Disambiguation Module for avoiding local optima, and (ii) a Future-Past Bidirectional Reasoning mechanism for globally coherent planning. Experiments on both simulated and real-robot environments demonstrate that our method significantly reduces per-step latency while achieving competitive or superior performance compared to panoramic-view baselines. These results demonstrate the practicality and effectiveness of Fast-SmartWay for real-world zero-shot embodied navigation.

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