Time-Division Multimodal Tactile Perception for Physical AI and Robotic Hands

Abstract

Equipping robotic end-effectors with human-like tactile perception is crucial for dexterous manipulation, requiring simultaneous thermal and mechanical sensing at the contact interface. Conventional multimodal sensors often rely on stacked or patterned layers, which increase device thickness, reduce conformability on curved robotic fingers, and introduce response delays. To address this, we present a time-division tactile perception platform tailored for robotic applications that utilizes memristive Ag-Cu 2 O core-sheath nanowire networks. This ultrathin artificial skin alternates between thermal and mechanical modalities at 16 Hz via memristive transitions, mirroring the processing of biological mechanoreceptors. In the SET state, sparse silver filaments form a mechanically sensitive network. During RESET, the semiconducting Cu 2 O sheath provides high thermal sensitivity. Lacking reactive components, the sensor achieves sub-microsecond mechanical and millisecond thermal responses, ideal for real-time robotic feedback. A deep learning pipeline processing these time-division signals improved object classification accuracy to 95%. Using a wireless module, 20 household objects were recognized with 83% accuracy. This single-layer architecture enables direct, seamless integration onto robotic hands, laying the groundwork for multimodal tactile intelligence in physical AI.