Skin‐Inspired Multi‐Modal Mechanoreceptors for Dynamic Haptic Exploration

TLDR

Active sensing in humans and animals relies on diverse skin mechanoreceptors to detect hardness, texture, and tackiness, whereas current robotic tactile sensors are limited to a few modalities. The study develops a skin‑inspired 3‑D mechanoreceptor (SENS) to enable closed‑loop dynamic haptic exploration by detecting multiple mechanical stimuli. A tensor‑based nonlinear model characterizes SENS’s 3‑D deformation, guiding the design and optimization of its multimodal sensing. The closed‑loop system using SENS achieved ≈96 % object recognition accuracy and demonstrates potential for autonomous learning, healthcare, and extreme‑environment exploration.

Abstract

Abstract Active sensing is a fundamental aspect of human and animal interactions with the environment, providing essential information about the hardness, texture, and tackiness of objects. This ability stems from the presence of diverse mechanoreceptors in the skin, capable of detecting a wide range of stimuli and from the sensorimotor control of biological mechanisms. In contrast, existing tactile sensors for robotic applications typically excel in identifying only limited types of information, lacking the versatility of biological mechanoreceptors and the requisite sensing strategies to extract tactile information proactively. Here, inspired by human haptic perception, a skin‐inspired artificial 3D mechanoreceptor (SENS) capable of detecting multiple mechanical stimuli is developed to bridge sensing and action in a closed‐loop sensorimotor system for dynamic haptic exploration. A tensor‐based non‐linear theoretical model is established to characterize the 3D deformation (e.g., tensile, compressive, and shear deformation) of SENS, providing guidance for the design and optimization of multimode sensing properties with high fidelity. Based on SENS, a closed‐loop robotic system capable of recognizing objects with improved accuracy (≈96%) is further demonstrated. This dynamic haptic exploration approach shows promise for a wide range of applications such as autonomous learning, healthcare, and space and deep‐sea exploration.