Human-Inspired Soft Anthropomorphic Hand System for Neuromorphic Object and Pose Recognition Using Multimodal Signals

Abstract

The human somatosensory system integrates multimodal sensory feedback, including tactile, proprioceptive, and thermal signals, to enable comprehensive perception and effective interaction with the environment. Inspired by the biological mechanism, we present a sensorized soft anthropomorphic hand equipped with diverse sensors designed to emulate the sensory modalities of the human hand. This system incorporates biologically inspired encoding schemes that convert multimodal sensory data into spike trains, enabling highly-efficient processing through Spiking Neural Networks (SNNs). By utilizing these neuromorphic signals, the proposed framework achieves 97.14% accuracy in object recognition across varying poses, significantly outperforming previous studies on soft hands. Additionally, we introduce a novel differentiator neuron model to enhance material classification by capturing dynamic thermal responses. Our results demonstrate the benefits of multimodal sensory fusion and highlight the potential of neuromorphic approaches for achieving efficient, robust, and human-like perception in robotic systems.