Audio-Based Tactile Human-Robot Interaction Recognition

TL;DR

This paper presents a novel audio-based method for recognizing tactile human-robot interactions using microphones and neural networks, offering an alternative to traditional force sensors.

Contribution

It introduces a new approach employing microphones and CNNs to classify tactile interactions on robots, reducing reliance on force sensors.

Findings

High accuracy in classifying different touch types

Effective use of audio signals for tactile recognition

Potential for simpler tactile sensing systems

Abstract

This study explores the use of microphones placed on a robot's body to detect tactile interactions via sounds produced when the hard shell of the robot is touched. This approach is proposed as an alternative to traditional methods using joint torque sensors or 6-axis force/torque sensors. Two Adafruit I2S MEMS microphones integrated with a Raspberry Pi 4 were positioned on the torso of a Pollen Robotics Reachy robot to capture audio signals from various touch types on the robot arms (tapping, knocking, rubbing, stroking, scratching, and pressing). A convolutional neural network was trained for touch classification on a dataset of 336 pre-processed samples (48 samples per touch type). The model shows high classification accuracy between touch types with distinct acoustic dominant frequencies.