arXiv cs.AI· Wenjun Huang, Shenghao Fu, Yian Jin, Yang Ni, Yifan Li, Lichi Li, Xingjian Diao, Pengfei Zhang, Yutong Song, Hanning Chen, Jiarui Cui, Tianyu Yang, Shilong Li, Yu Kong, Mohsen Imani·· 3 天前AI 评分22
ADAPT：面向熄灯工业导航的音频-深度动作规划与过渡感知奖励
ADAPT: Audio-Depth Action Planning with Transition-Aware Rewards for Lights-Out Industrial Navigation
AI 导读
针对熄灯工业环境中可见光缺失的问题，研究者提出音频-深度动作规划框架 ADAPT，用空间音频定位目标、深度感知障碍几何，并通过强化学习优化闭环导航。其核心贡献 TARS 在位置-朝向图上定义姿态感知的最短动作势函数，兼顾可行移动与原地旋转。
正文 · 原文
Authors:Wenjun Huang, Shenghao Fu, Yian Jin, Yang Ni, Yifan Li, Lichi Li, Xingjian Diao, Pengfei Zhang, Yutong Song, Hanning Chen, Jiarui Cui, Tianyu Yang, Shilong Li, Yu Kong, Mohsen Imani
Abstract:Vision-language-action models for embodied navigation typically rely on RGB observations, limiting their applicability in lights-out industrial environments where visible illumination may be unavailable. We study illumination-independent navigation using spatial audio for target localization and depth for obstacle-aware geometry. To support this setting, we introduce DarkNav-Bench, a benchmark with 24 industrial scenes and four progressively challenging acoustic conditions. We further propose ADAPT, an audio-depth action-planning framework equipped with spatial-audio perception and optimized for closed-loop navigation through reinforcement learning (RL). Our key RL contribution, transition-aware reward shaping (TARS), defines a pose-aware shortest-action potential over a position-heading graph. Unlike conventional geodesic-progress rewards, TARS accounts for both feasible movements and in-place rotations, providing action-consistent feedback for policy optimization. Experiments show that ADAPT consistently outperforms training-based navigation baselines and zero-shot multimodal models on both seen and unseen scenes. On unseen environments, it achieves 90.6% success under Clean conditions and retains 60.0% success under the most challenging Localized Noise setting. These results demonstrate the potential of spatial audio and depth as complementary modalities for reliable RGB-free navigation in lights-out industrial environments.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.15956 [cs.AI] |
| (or arXiv:2610.15956v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.15956 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Wenjun Huang [view email]
[v1]
Sun, 11 Oct 2026 22:23:04 UTC (11,820 KB)
来源：arXiv cs.AI · arxiv.org