从指令到事件：基于声音的移动操作
From Instruction to Event: Sound-Triggered Mobile Manipulation
AI总结：
本文提出基于声音的移动操作方法，通过开发Habitat-Echo数据平台和任务规划器，使移动代理能主动感知并响应声音事件，无需显式指令。
摘要：
当前移动操作研究主要采用指令驱动范式，但此模式限制了代理的自主性和对动态环境事件的反应能力。为解决这些问题，我们引入了基于声音的移动操作，要求代理主动感知并交互声音源。为此，我们开发了Habitat-Echo数据平台，集成了声学渲染与物理交互。我们进一步提出包含高层任务规划器和低层策略模型的基线方案。大量实验表明，所提基线使代理能够主动检测并响应听觉事件，无需逐案指令。值得注意的是，在挑战性的双源场景中，代理成功从重叠声学干扰中分离出主要源以执行首次交互，随后继续操作次级物体，验证了基线的鲁棒性。
英文摘要：
Current mobile manipulation research predominantly follows an instruction-driven paradigm, where robots rely on predefined textual commands to execute tasks. However, this setting confines robots to a passive role, limiting robotic autonomy and the ability to react to dynamic environmental events. To address these limitations, we introduce Sound-Triggered Mobile Manipulation (STMM), where robots must actively perceive and interact with sound-emitting objects without explicit action instructions. To support STMM, we develop Habitat-Echo, a simulation platform that integrates sound rendering with physical interaction. We further propose a hierarchical baseline that translates high-level planning into low-level executions, where a task planner predicts a skill chain for policy models to execute sequentially. Experiments indicate the feasibility of perceiving auditory events and executing corresponding physical interactions without explicit instructions. Notably, in challenging multi-event scenarios, the robot successfully isolates the primary sources from overlapping acoustic interference to execute the first interactions, and subsequently proceeds to manipulate the secondary objects. These new challenges of planning-to-execution position STMM as a measurable research direction.The code and datasets will be released upon acceptance.
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