发表机构
Institute of Science Tokyo（东京科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
BCNav提出解耦框架，通过DOA估计分离声学与导航策略，仅用深度图像和方位角进行模仿学习，输出连续速度指令，实现无需声学微调的物理机器人声源导航。
AI 中文摘要
导航朝向声源的能力扩展了机器人在其视觉范围之外的触达能力，使其能够在未知环境中响应听觉事件。为使机器人具备此能力，现有方法通过声学模拟器中的联合视听学习来耦合声学与视觉信息。然而，声学模拟保真度低且成本高昂，产生的领域差距阻碍了可靠的现实世界部署，同时，从基于网格的模拟器继承的离散动作空间在物理机器人上引入了额外的运动学差距。为缓解这些问题，我们提出BCNav，一种解耦框架，通过到达方向（DOA）估计将声学模块与学习到的导航策略分离：估计器提供到声源的标量方位，因此导航策略仅处理深度图像和方位角，这两个输入的领域差距已得到充分表征。我们收集带有校准方位噪声注入的最短路径演示，并通过模仿学习训练策略，以输出可直接在地面机器人上执行的连续速度指令。我们在模拟环境和物理机器人上演示了该方法，无需任何声学微调、先验地图或真实世界音频数据收集即可导航未知环境。代码可在以下网址获取：此https URL。
英文摘要
The ability to navigate toward sound sources extends a robot's reach beyond its visual field, enabling response to auditory events in unknown environments. To equip robots with this capability, existing methods couple acoustic and visual information through joint audio-visual learning in acoustic simulators. However, acoustic simulation is both low-fidelity and expensive, producing a domain gap that prevents reliable real-world deployment, while the discrete action spaces inherited from grid-based simulators introduce an additional kinematic gap on physical robots. To alleviate these issues, we propose BCNav, a decoupled framework that separates the acoustic module from the learned navigation policy using direction-of-arrival (DOA) estimation: an estimator provides a scalar bearing to the sound source, so the navigation policy only processes depth images and a bearing angle, two inputs whose domain gaps are well characterized. We collect shortest-path demonstrations with calibrated bearing noise injection and train the policy via imitation learning to output continuous velocity commands directly executable on ground robots. We demonstrate the method in simulation and on a physical robot, navigating unknown environments without any acoustic fine-tuning, prior mapping, or real-world audio data collection. Code is available at https://github.com/york1to/bcnav.
Comments6 pages, 5 figures, 2 tables. Accepted to IEEE RO-MAN 2026
