As humans, we hear sound every second of our life. The sound we hear is often affected by the acoustics of the environment surrounding us. For example, a spacious hall leads to more reverberation. Room Impulse Responses (RIR) are commonly used to characterize environment acoustics as a function of the scene geometry, materials, and source/receiver locations. Traditionally, RIRs are measured by setting up a loudspeaker and microphone in the environment for all source/receiver locations, which is time-consuming and inefficient. We propose to let two robots measure the environment's acoustics by actively moving and emitting/receiving sweep signals. We also devise a collaborative multi-agent policy where these two robots are trained to explore the environment's acoustics while being rewarded for wide exploration and accurate prediction. We show that the robots learn to collaborate and move to explore environment acoustics while minimizing the prediction error. To the best of our knowledge, we present the very first problem formulation and solution to the task of collaborative environment acoustics measurements with multiple agents.

翻译：作为人类，我们每时每刻都在感知声音，而所听到的声音往往受到周围环境声学特性的影响。例如，宽敞的大厅会导致更强的混响现象。房间脉冲响应（RIR）通常用于表征环境声学特性，其取决于场景几何结构、材料属性以及声源/接收器的位置。传统上，RIR的测量需要在环境中为所有声源/接收器位置架设扬声器和麦克风，这一过程耗时且效率低下。我们提出让两个机器人通过主动移动并发射/接收扫频信号来测量环境的声学特性。同时，我们设计了一种协作式多智能体策略：通过奖励机制引导两个机器人探索环境声学特性（奖励基于广域探索与精确预测），训练其协同作业。实验表明，机器人能够学会协作移动以探索环境声学特性，同时最小化预测误差。据我们所知，这是首个针对多智能体协作环境声学测量任务的问题定义与解决方案。