Finger-level tactile intelligence toward robotic dexterous operation via afferent encoding and neuronal multiplexing

Robotic dexterous operation necessitates a tactile sensing system that possesses capabilities equivalent to those of the human finger, but this benchmark remains unfulfilled by current sensors. Here, we establish a frequency-separated afferent encoding and neuronal multiplexing mechanism that fundamentally breaks from the conventional image-processing paradigm of visuotactile sensors. This mechanism enables, for the first time, human finger-level tactile intelligence by seamlessly integrating the recognition of contact force, surface roughness, and slip, which is implemented in a sensor named HydroPalm. HydroPalm achieves force perception with remarkable accuracy (R² = 0.965), producing force heatmaps strikingly similar to a human finger. Moreover, it demonstrates finger-level acuity in resolving micro-scale textures with a roughness average (Ra) of 0.258 μm, far surpassing existing tactile sensors. Its millisecond-level latency (< 75 ms) allows predictive detection of incipient slip, facilitating human-like reflexive grip adjustments. The pioneering finger-level tactile perception endows the underwater robotic pipe assembly with force perception, texture recognition, and slip prediction. This establishes a foundation for integrating human-level tactile intelligence into robotic dexterous manipulation.