Consequential Motor Sounds Of Robots

Research into the acoustic perceptions of robots and how the mechanical sounds they make effect peoples perception of their ability. Published work at HRI 2017 and Ro-Man 2017.

How do sounds shape our interactions with robots? My co-author Dylan Moore and I worked together to conduct research into how the consequential sounds that robots generate (e.g from their motors) influence the way in which people perceive the robot. Our goal is to do fundamental research that designers and engineers can utilise in the workplace when making decisions about products they are making that generate sound. We believe this research is the first published research to concretely show that the consequential sounds a robot makes affects peoples perception of the robot .

The video above gives a brief introduction into the motivation behind the research.

Our initial study was looking at the audio from 20 servo motors in isolation, characterising the sound acoustically and perceptually. This work was written up and published at HRI (Human Robot Interaction) 2017.

Building on the findings from the first study, the above video introduces the motivations behind our follow up study which was published at Ro-Man 2017.

Our second study looked at how the sound a robot arm made affected how people perceieved the arm. This study showed that a lower cost arm (‘worse’) sound  improved how competent the arm was seen to be in certain contexts. This came at a tradeoff of aesthetics which was significantly decreased. These studies are the first time anyone in the robotics field has shown direct correlation between the motor sounds a robot makes and it’s perceived charateristics.

Click To Read:

Making Noise Intentional: A Study of Servo Sound Perception. Moore, D., Tennent, H., Martelaro, N., & Ju, W. (2017, March). In Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (pp. 12-21). ACM.

Good Vibrations: How Consequential Sounds Affect Perception of Robotic Arms. Tennent, H., Moore, D., Jung, M., & Ju, W.

© Hamish Tennent 2017