Soft grippers have shown promising applications in robotics due to their high flexibility, damage-free contact, and environmental adaptability. However, their sensing often relies on external sensors and thus suffers from susceptibility to environmental interference. Here, we report a liquid crystal elastomer (LCE) gripper integrated with dual-mode triboelectric nanogenerators (TENGs) for self-powered target identification. By synergizing fluorinated ethylene propylene (FEP) and polydimethylsiloxane (PDMS) TENG sensors, the system generates voltage signals (V1, V2) encoding intrinsic material properties and kinematic parameters during object interactions. The hybrid convolutional neural network-long short-term memory (CNN-LSTM) architecture extracts discriminative spatiotemporal features from raw triboelectric/electrostatic signatures, achieving 94.4% classification accuracy across 5 material categories through cross-validation. This fusion of contact electrification physics and deep learning overcomes traditional limitations in environmental interference susceptibility, establishing a paradigm for perceptually intelligent soft robotics in industrial automation and human-machine interaction scenarios.
Chen et al. (Thu,) studied this question.