Non-Contact Tactile Perception in Human-Robot Interaction: Deep Learning-Enhanced Super-Resolution Spatial Sensing

Abstract

With the increasing deployment of robots in dynamic and unpredictable scenarios, it becomes necessary for robots to acquire not only contact-based but also non-contact tactile signals to enhance environmental understanding. However, current non-contact tactile sensors are largely limited to detecting or coarsely recognizing external stimuli, while achieving high spatial resolution typically entails increased sensor density and complex fabrication. This work presents a flexible sparse 2D sensor array, in conjunction with a tailored deep learning model called adaptive spatial-temporal graph convolutional network (ASTGCN), facilitating 3D spatial super-resolution (SR) perception. Built on single-electrode triboelectric nanogenerators with an optimized layout, the sensor array achieves spatial perception while providing a large perception space at low sensor density. Enhanced by the ASTGCN model, this system achieves an average spatial positioning error of 3.11 mm with a physical resolution of only 23 sensors. This research provides novel insights into non-contact haptic perception systems, enabling spatial super-resolution tasks, including spatial trajectory tracking and non-contact gesture classification with 99.33% accuracy, where the gesture classification is used to control a dexterous hand for human-robot interaction.