As robots move through space, there is often a need to make people aware of their presence to ensure efficient navigation and avoid blocks or collisions. Navigation sounds can contribute to this end. However, sound can also become a disturbance to people in shared spaces. This paper introduces a concept of adaptive sound for robot navigation that aims to produce sounds that are aligned with the robot’s characteristics, audible over ambient noise, and acceptable in different social contexts. We describe the design process we followed, including a controlled user experiment and an in- situ study that explore how people adjust sounds to different contexts using a strategy of sound frequency adaptation. Our controlled experiment provides preliminary insights into the potential of adaptive navigation sounds, although it does not yet identify a single optimal method for adjusting the sound. The results of our user studies also provide preliminary insights into relevant contextual factors to be considered when designing robot navigation sounds, and robot sounds in general. Our research is a first step toward the design of adaptive navigation sounds for human-robot interaction that support the seamless integration of robots in shared spaces.
Exploring adaptive navigation sound for indoor delivery robots
| Naoko Abe, Danilo Gallo, Baptiste Mouton, Mattia Racca, Tommaso Colombino, Dagyeong Lee |
| The 35th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Fukuoka, Japan, 24-28 August, 2026 |
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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