A Neuromorphic Approach to Slip Detection with 3D Magnetic Sensors

Abstract

Robust slip detection remains a major challenge in robotic manipulation, as it is essential for stable and adaptive interaction with objects of varying shapes, textures, and compliance. This thesis proposes a novel approach by designing a biologically inspired algorithm for real-time, asynchronous slip detection using low-cost, three-dimensional magnetic tactile sensors, while explicitly avoiding computationally heavy learning-based approaches.

To achieve this, temporal signal processing constrained to first-order filters and saturation functions was combined with a network of simulated spiking neurons (Ribar et al. 2019), inspired by biological tactile sensing and designed for compatibility with future neuromorphic CMOS hardware. The algorithm was implemented as a ROS2 node to enable integration into robotic systems and was first evaluated in a controlled environment, followed by a more qualitative in scenarios inspired by real-world conditions.

The results show that the proposed system can detect incipient slip with a latency below 30 ms, a performance comparable to human tactile response times and to state-of-the-art learning-based methods, which typically rely on higher-cost tactile sensors such as GelSight Mini (Jawale et al. 2024), or uSkin (Yan et al. 2022), whereas our approach uses low-cost, mass-manufacturable 3D magnetic sensors. Gross slip detection, however, was only achieved in controlled experiments at relatively high slip speeds. Robustness to noise during non-contact phases was improved through the inclusion of a contact-detection neuron. While the approach avoids computationally heavy learning methods, the ROS2 implementation required multi-threading to operate correctly, indicating that its computational requirements are reduced but not yet minimal.

Despite these promising outcomes, several limitations were observed, including reduced sensitivity on smooth surfaces, occasional false positives, and difficulties in generalizing parameter settings across different objects and surface textures. In addition, hardware constraints restricted the simulation speed of neurons, thereby limited their spiking frequencies, which in turn prevented further improvements in delay and sensitivity.

In conclusion, this work establishes a proof-of-concept for biologically inspired slip detection in robotics. It demonstrates the feasibility of detecting slip events using low-cost sensors and a network of simulated spiking neurons with asynchronous, event-driven computation, laying the groundwork for future implementations on low-power neuromorphic hardware.