Self-mixing interferometry (SMI) has been lauded for its sensitivity in detecting microvibrations, while requiring no physical contact with its target. In robotics, microvibrations have traditionally been interpreted as a marker for object slip, and recently as a salient indicator of extrinsic contact. We present the first-ever robotic fingertip making use of SMI for slip and extrinsic contact sensing. The design is validated through measurement of controlled vibration sources, both before and after encasing the readout circuit in its fingertip package. Then, the SMI fingertip is compared to acoustic sensing through four experiments. The results are distilled into a technology decision map. SMI was found to be more sensitive to subtle slip events and significantly more resilient against ambient noise. We conclude that the integration of SMI in robotic fingertips offers a new, promising branch of tactile sensing in robotics. Design and data files are available at https://github.com/RemkoPr/icra2025-SMI-tactile-sensing.

翻译：自混合干涉技术因其在检测微振动方面的高灵敏度而备受赞誉，且无需与目标物体发生物理接触。在机器人学中，微振动传统上被解读为物体滑移的标志，最近更被视为外部接触的显著指标。我们首次提出了一种利用自混合干涉技术进行滑移及外部接触感知的机器人指尖传感器。该设计通过测量受控振动源得到验证，验证过程涵盖该读出电路封装于指尖结构之前与之后两个阶段。随后，通过四项实验将自混合干涉指尖传感器与声学传感技术进行对比。实验结果被提炼为一项技术决策图谱。研究发现，自混合干涉技术对细微滑移事件更为敏感，且对环境噪声具有显著更强的抗干扰能力。我们得出结论：将自混合干涉技术集成于机器人指尖，为机器人触觉传感开辟了一条崭新且前景广阔的技术路径。设计文件与数据文件可通过 https://github.com/RemkoPr/icra2025-SMI-tactile-sensing 获取。