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Xiao Lu

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5 papers
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5

IROS Conference 2025 Conference Paper

Self-Sensing Liquid Crystal Elastomer Actuator with Magnetic-Thermal Synergy

  • Shen Gao
  • Mingjun Tang
  • Xiao Lu
  • Chenghao Zhou
  • Yuyin Zhang
  • Tao Yue
  • Yue Wang

Fueled by the rapid evolution of robotics, the demand for intelligent and lightweight robotic systems continues to grow across industries. However, conventional designs often separate sensing and actuation, resulting in structural complexity and diminished reliability. While integrated sensor-actuator systems offer a promising solution, they face significant challenges in manufacturing and scalability. Liquid crystal elastomer (LCE) are widely utilized in actuators for their thermally responsive deformation and programmability, while Neodymium-Iron-Boron (NdFeB) nanoparticles provide exceptional magnetic properties for sensing. This paper introduces a novel Self-Sensing LCE (SS-LCE) actuator, seamlessly combining LCE and NdFeB to enable simultaneous actuation and self-sensing capabilities. Under thermal stimulation, the actuator executes complex motions while delivering real-time feedback through magnetic field variations. Its programmability and adaptable fabrication process support diverse motion modes, unlocking broad application potential. By enhancing integration, reliability, and flexibility, this self-sensing actuator represents a pivotal advancement in the development of lightweight, intelligent robotic systems with significant research and industrial implications.

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IJCAI Conference 2018 Conference Paper

Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification

  • Zhao Kang
  • Xiao Lu
  • Jinfeng Yi
  • Zenglin Xu

Multiple kernel learning (MKL) method is generally believed to perform better than single kernel method. However, some empirical studies show that this is not always true: the combination of multiple kernels may even yield an even worse performance than using a single kernel. There are two possible reasons for the failure: (i) most existing MKL methods assume that the optimal kernel is a linear combination of base kernels, which may not hold true; and (ii) some kernel weights are inappropriately assigned due to noises and carelessly designed algorithms. In this paper, we propose a novel MKL framework by following two intuitive assumptions: (i) each kernel is a perturbation of the consensus kernel; and (ii) the kernel that is close to the consensus kernel should be assigned a large weight. Impressively, the proposed method can automatically assign an appropriate weight to each kernel without introducing additional parameters, as existing methods do. The proposed framework is integrated into a unified framework for graph-based clustering and semi-supervised classification. We have conducted experiments on multiple benchmark datasets and our empirical results verify the superiority of the proposed framework.

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