# ADAPT：面向无照明工业导航的带转换感知奖励的音频-深度动作规划

> 原文链接：https://robotics.hellobike.com/papers/2026-10-11-2610-15956
> 频道：papers | 更新日期：2026-10-11 | 标签：arxiv, cs.RO
> 核心摘要：针对无可见照明工业导航，ADAPT结合空间音频与深度并用TARS强化学习，在DarkNav-Bench上验证。

# ADAPT：面向无照明工业导航的带转换感知奖励的音频-深度动作规划

::: v-pre
> 原文链接：[ADAPT: Audio-Depth Action Planning with Transition-Aware Rewards for Lights-Out Industrial Navigation](https://arxiv.org/abs/2610.15956)（arXiv 2610.15956 · 发布日期：2026-10-11）

**ADAPT: Audio-Depth Action Planning with Transition-Aware Rewards for Lights-Out Industrial Navigation**

针对无可见照明工业导航，ADAPT结合空间音频与深度并用TARS强化学习，在DarkNav-Bench上验证。

## 核心要点

- 现有具身导航VLA模型通常依赖RGB观测，限制了其在无可见照明工业环境中的适用性。
- 论文引入DarkNav-Bench，包含24个工业场景和四种逐步变难的声学条件。
- ADAPT结合空间音频感知与深度几何，通过强化学习优化闭环导航动作规划。
- TARS在位置-朝向图上定义姿态感知最短动作势函数，实验中ADAPT优于训练式基线和零样本多模态模型。

**验证范围与局限**：验证范围为DarkNav-Bench中的已见和未见工业场景及四种声学条件。

## 英文摘要 (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.

作者：Wenjun Huang, Shenghao Fu, Yian Jin, Yang Ni, Yifan Li, Lichi Li, Xingjian Diao, Pengfei Zhang, Yutong Song, Hanning Chen
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## 原始参考来源 (Sources)

- [arXiv 2610.15956：ADAPT: Audio-Depth Action Planning with Transition-Aware Rewards for Lights-Out Industrial Navigation](https://arxiv.org/abs/2610.15956)