用机器人身上的麦克风听声音，识别用户怎么碰它。
Audio-Based Tactile Human-Robot Interaction Recognition
- 在机器人躯干装麦克风，通过声音区分触摸类型。
- 模型对6种触碰分类准确率高，依赖声音主频差异。
- 适合无传感器或低成本触觉交互场景，如教育机器人。
本研究探索将麦克风置于机器人身体上，通过捕捉机器人外壳被触碰时产生的声音来检测触觉交互，作为传统关节扭矩传感器或六轴力/力矩传感器的替代方案。在Pollen Robotics Reachy机器人手臂上，使用两个Adafruit I2S MEMS麦克风与Raspberry Pi 4集成，采集六种触摸类型（轻敲、敲击、摩擦、抚摸、抓挠、按压）的声音信号。基于336个预处理样本（每类48个）训练卷积神经网络进行触碰分类，结果显示不同触碰类型在声学主频上存在显著差异，模型可实现高精度分类。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。