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This study explores the significance of robot hearing systems, emphasizingtheir importance for robots operating in diverse and uncertain environments. Itintroduces the hardware design principles using robotaxis as an example, whereexterior microphone arrays are employed to detect sound events such as sirens.The challenges, goals, and test methods are discussed, focusing on achieving asuitable signal-to-noise ratio (SNR). Additionally, it presents a preliminarysoftware framework rooted in probabilistic robotics theory, advocating for theintegration of robot hearing into the broader context of perception anddecision-making. It discusses various models, including Bayes filters,partially observable Markov decision processes (POMDP), and multiagent systems,highlighting the multifaceted roles that robot hearing can play. In conclusion,as service robots continue to evolve, robot hearing research will expand,offering new perspectives and challenges for future development beyond simplesound event classification.

Building Ears for Robots: Machine Hearing in the Age of Autonomy