Selected work

Representative Papers

MROPE: A Multi-Robot Safe Cooperative Strategy via combined Predictive Safety Filters and Ellipse-based Constraint Compression

Jul 31, 2026

This work addresses the challenges of real-time performance and safe collision avoidance in multi-robot systems when tracking dynamic targets in complex environments. The authors propose a hierarchical cooperative framework wherein high-level coordination leverages distributed consensus optimization for scalable task allocation, while a low-level predictive safety filter (PSF) ensures local obstacle avoidance. A key innovation lies in dynamically aggregating multiple obstacles into a single safety ellipse, coupled with ellipse constraint compression to substantially reduce computational complexity. Experimental results demonstrate that the proposed approach outperforms centralized baselines in both simulated and real-world scenarios, achieving strict safety guarantees while significantly enhancing real-time responsiveness and system scalability.

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Explainable Condition Monitoring via Probabilistic Anomaly Detection Applied to Helicopter Transmissions

Mar 09, 2026

This work addresses the challenge of scarce fault data in safety-critical applications such as helicopter transmission systems by proposing an interpretable anomaly detection method that relies solely on healthy operational data. The approach employs Bayesian probabilistic modeling to learn the distribution of normal system states and introduces a tailored anomaly metric for real-time fault warning. By integrating uncertainty quantification with a visualization-based explanation mechanism, the method enhances the trustworthiness of diagnostic decisions. Experimental evaluation on both public predictive maintenance benchmarks and multi-year real-world helicopter transmission datasets demonstrates that the proposed technique achieves state-of-the-art detection performance while offering clear interpretability, making it well-suited for industrial settings demanding high reliability.

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Secure and practical Quantum Digital Signatures

Aug 07, 2025

Quantum computing poses a severe threat to classical digital signature schemes. Method: This paper proposes an information-theoretically secure quantum-resistant digital signature scheme. It constructs a practical signing protocol leveraging pre-shared keys generated via quantum key distribution, combined with universal hash families and information-theoretically secure authentication. For the first time, it provides a rigorous information-theoretic security proof under a realistic model permitting authentication failures, and systematically rectifies three critical security flaws in prior protocols. Contributions: (1) Theoretically, it establishes the first failure-tolerant information-theoretic security framework; (2) Practically, it significantly reduces pre-shared bit consumption and signature length through parameter optimization, enhancing signing efficiency; (3) Implementation-wise, it delivers an optimal protocol configuration that jointly ensures information-theoretic security, practical deployability, and quantum resistance.

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Measuring Uncertainty in Shape Completion to Improve Grasp Quality

Apr 22, 2025

Single-view point cloud shape completion for robotic grasping suffers from model uncertainty, leading to grasp failures. Method: This paper proposes a novel inference-time 3D shape completion uncertainty quantification method and—first in the literature—explicitly incorporates this uncertainty into the grasp pose quality scoring function. Uncertainty is estimated via Monte Carlo Dropout, and an uncertainty-weighted quality evaluation model is constructed. Physical experiments are conducted on a 7-DOF robotic arm. Contribution/Results: In real-world household object grasping tasks, the proposed uncertainty-aware ranking strategy significantly improves the success rate of the top-5 grasp candidates, outperforming state-of-the-art uncertainty-agnostic approaches. This work establishes a new paradigm for uncertainty-driven embodied intelligent decision-making.

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Adaptive Synaptogenesis Implemented on a Nanomagnetic Platform

Apr 14, 2025

This work addresses catastrophic forgetting in artificial neural networks (ANNs) during continual learning by proposing a brain-inspired adaptive synaptogenesis mechanism that emulates supervised Hebbian learning coordinated between the neocortex and hippocampus. Methodologically, it formalizes biological synaptogenesis for the first time as a hardware-deployable, sparse, gated learning framework, wherein dynamic hippocampal gating regulates synaptic formation—departing from conventional static-weight ANN paradigms. Integrating neuromorphic algorithm design, SPICE-level nanomagnetic device modeling, and a custom accelerator architecture, the approach enables high-density, ultra-low-power synaptic plasticity operations. Experimental results demonstrate a 37% reduction in error rate on continual learning benchmarks. A 16-nm hardware prototype achieves per-synapse energy consumption below 1 aJ and synaptic density exceeding 10¹²/cm².

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Recent publications

Latest Papers

MROPE: A Multi-Robot Safe Cooperative Strategy via combined Predictive Safety Filters and Ellipse-based Constraint Compression

Jul 31, 2026

This work addresses the challenges of real-time performance and safe collision avoidance in multi-robot systems when tracking dynamic targets in complex environments. The authors propose a hierarchical cooperative framework wherein high-level coordination leverages distributed consensus optimization for scalable task allocation, while a low-level predictive safety filter (PSF) ensures local obstacle avoidance. A key innovation lies in dynamically aggregating multiple obstacles into a single safety ellipse, coupled with ellipse constraint compression to substantially reduce computational complexity. Experimental results demonstrate that the proposed approach outperforms centralized baselines in both simulated and real-world scenarios, achieving strict safety guarantees while significantly enhancing real-time responsiveness and system scalability.

0 citationsRead paper

Explainable Condition Monitoring via Probabilistic Anomaly Detection Applied to Helicopter Transmissions

Mar 09, 2026

This work addresses the challenge of scarce fault data in safety-critical applications such as helicopter transmission systems by proposing an interpretable anomaly detection method that relies solely on healthy operational data. The approach employs Bayesian probabilistic modeling to learn the distribution of normal system states and introduces a tailored anomaly metric for real-time fault warning. By integrating uncertainty quantification with a visualization-based explanation mechanism, the method enhances the trustworthiness of diagnostic decisions. Experimental evaluation on both public predictive maintenance benchmarks and multi-year real-world helicopter transmission datasets demonstrates that the proposed technique achieves state-of-the-art detection performance while offering clear interpretability, making it well-suited for industrial settings demanding high reliability.

0 citationsRead paper

Secure and practical Quantum Digital Signatures

Aug 07, 2025

Quantum computing poses a severe threat to classical digital signature schemes. Method: This paper proposes an information-theoretically secure quantum-resistant digital signature scheme. It constructs a practical signing protocol leveraging pre-shared keys generated via quantum key distribution, combined with universal hash families and information-theoretically secure authentication. For the first time, it provides a rigorous information-theoretic security proof under a realistic model permitting authentication failures, and systematically rectifies three critical security flaws in prior protocols. Contributions: (1) Theoretically, it establishes the first failure-tolerant information-theoretic security framework; (2) Practically, it significantly reduces pre-shared bit consumption and signature length through parameter optimization, enhancing signing efficiency; (3) Implementation-wise, it delivers an optimal protocol configuration that jointly ensures information-theoretic security, practical deployability, and quantum resistance.

0 citationsRead paper

Measuring Uncertainty in Shape Completion to Improve Grasp Quality

Apr 22, 2025

Single-view point cloud shape completion for robotic grasping suffers from model uncertainty, leading to grasp failures. Method: This paper proposes a novel inference-time 3D shape completion uncertainty quantification method and—first in the literature—explicitly incorporates this uncertainty into the grasp pose quality scoring function. Uncertainty is estimated via Monte Carlo Dropout, and an uncertainty-weighted quality evaluation model is constructed. Physical experiments are conducted on a 7-DOF robotic arm. Contribution/Results: In real-world household object grasping tasks, the proposed uncertainty-aware ranking strategy significantly improves the success rate of the top-5 grasp candidates, outperforming state-of-the-art uncertainty-agnostic approaches. This work establishes a new paradigm for uncertainty-driven embodied intelligent decision-making.

0 citationsRead paper

Adaptive Synaptogenesis Implemented on a Nanomagnetic Platform

Apr 14, 2025

This work addresses catastrophic forgetting in artificial neural networks (ANNs) during continual learning by proposing a brain-inspired adaptive synaptogenesis mechanism that emulates supervised Hebbian learning coordinated between the neocortex and hippocampus. Methodologically, it formalizes biological synaptogenesis for the first time as a hardware-deployable, sparse, gated learning framework, wherein dynamic hippocampal gating regulates synaptic formation—departing from conventional static-weight ANN paradigms. Integrating neuromorphic algorithm design, SPICE-level nanomagnetic device modeling, and a custom accelerator architecture, the approach enables high-density, ultra-low-power synaptic plasticity operations. Experimental results demonstrate a 37% reduction in error rate on continual learning benchmarks. A 16-nm hardware prototype achieves per-synapse energy consumption below 1 aJ and synaptic density exceeding 10¹²/cm².

0 citationsRead paper