Study showcases enhanced sensory signal fusion in self-powered neuromorphic systems, indicating promising advancements for robotics.
Artificial soft perception systems that integrate multifield sensing and neuromorphic processing in a compact energy-efficient manner are crucial for embodied intelligence and human-machine interface. However, achieving seamless fusion and identification of multiple sensory signals within a single device with power autonomy remains a challenge. Here, inspired by earthworm skin, we present a self-powered multistimuli neuromorphic vision skin (SMNV-S). This system has a homogeneous ion heterogel array that serves as a multiresponse receptor, capable of simultaneously transducing light, heat, tactile, and humidity into neuroelectric signals without external bias, via spatial ionic redistribution within the gel. These fused signals can be clearly distinguished for stimulus identification, depending on the response intensity and learning/delay time. By utilizing these receptors as multisensory nerves, SMNV-S achieves not only spatial sensing of target geometries but also temporal coding of multisensory data fusion. This allows the system to mimic cross-modal visual perception, demonstrating image learning enhancement or depression modulated by other field inputs. This work provides a versatile multifield-driven neurosensing platform for adaptive information interaction in a complex environment.
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Chen et al. (2026) studied this question.
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