Self-Mixing Laser Interferometry for Robotic Tactile Sensing

TL;DR

This work introduces the first robotic fingertip that uses self-mixing interferometry (SMI) for slip and extrinsic contact sensing, offering non-contact vibrometry with high sensitivity to microvibrations. The authors develop a compact fingertip that embeds an SMI readout alongside a microphone, validate the sensing modality through interferometry and vibrometry tests, and compare performance against acoustic sensing across four manipulation tasks. Key findings show SMI excels at detecting subtle slip and remains robust to broadband ambient noise, though microphones can outperform SMI in some slip scenarios and clearly audible contacts. The combination of a low-cost, scalable SMI design and demonstrated noise resilience suggests a promising direction for tactile sensing in robotics, with open-source design files enabling adoption and further study. $\text{SMI}$ provides a non-contact, vibrometry-based alternative to acoustic sensing, potentially easing perception in cluttered or noisy environments and enabling richer tactile-enabled learning tasks.

Abstract

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.

Figures (12)

• Figure 1: Self-mixing interferometry. Every time the target displacement D changes by $\lambda$/2, a discontinuity or fringe appears in the SMI signal. In the example, a D of 4$\lambda$ peak-to-peak leads to 8 discontinuities in I$_{\mathrm{PD}}$.
• Figure 2: The readout circuit consists of a transimpedance amplifier (TIA), a high-pass (HP) filter, a signal amplifier (SA), a low-pass anti-aliasing (AA) filter, and an analog-to-digital converter (ADC).
• Figure 3: Mould for silicone pour.
• Figure 4: Front and back views of the fingertips mounted to a Robotiq 2F-85.
• Figure 5: Section views.