Lingling Hu

Nanjing University of Aeronautics and Astronautics, Sun Yat-sen University, Tongji University, Beijing Institute of Technology

Papers

Total Citations

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About

Lingling Hu is an innovative researcher working at the intersection of mechanical metamaterials and computer vision. Her work spans two distinct but equally impactful domains: the design of novel compression-torsion metamaterials and advanced deep learning for robotic perception. In the field of mechanical metamaterials, Hu’s most-cited paper, “Decoupling Poisson’s ratio effect from compression-torsion metamaterial” (2025, 14 citations), introduces a groundbreaking approach to controlling material deformation, enabling the creation of a new type of overrunning clutch based on curved-plate structures (2024, 8 citations). These contributions have significant implications for advanced mechanical systems and robotics. Simultaneously, Hu has made notable strides in computer vision, developing an unsupervised monocular depth estimation method that aggregates image features with wavelet SSIM loss (2021, 6 citations), published in *Intelligence & Robotics*. Her most recent work, “Cross-modal State Space Modeling for Real-time RGB-thermal Wild Scene Semantic Segmentation” (2025, 2 citations), addresses the computational challenges of integrating RGB and thermal data for field robots, proposing an efficient alternative to Transformer-based approaches. With a growing citation record and work published in high-impact venues, Lingling Hu is establishing herself as a versatile researcher whose innovations in both materials science and AI-driven robotics are poised to influence future technologies.