Selected work

Representative Papers

Metric-Based Equilibrium Selection in Noncooperative Differentiable Games

Oct 05, 2026

This study addresses the problem of selectively attracting specific equilibria in non-cooperative differentiable games without altering their equilibrium locations. To this end, it proposes a conditioning framework based on state-dependent symmetric positive-definite metrics, which achieves targeted equilibrium control by smoothly interpolating between stabilizing and destabilizing strategies. The authors demonstrate that player-independent metrics fail to preserve the stability of differential Nash equilibria, and accordingly construct a unified single metric field that retains all equilibria while prescribing their local stability types. The effectiveness of this approach is successfully validated through experiments on a continuous-commitment Stag Hunt game and an entropy-regularized Iterated Prisoner’s Dilemma.

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The Neuro-Physical Inverter: A Modular Framework for Magnetotelluric Inversion Coupling Ensemble Conditioning with Residual Learning

Oct 02, 2026

This study addresses the challenge of simultaneously achieving uncertainty quantification and physical constraint enforcement in magnetotelluric inversion. To this end, we propose a Neural Physics Inference (NPI) framework that pioneers the coupling of an Ensemble Conditional Gaussian Process (EnsCGP) with constrained residual learning, incorporating a physics-constrained objective function to establish a modular, uncertainty-aware inversion paradigm. Crucially, this mechanism ensures the propagation of ensemble uncertainty information throughout the entire estimation process. Evaluations on both synthetic datasets and field measurements from the Gabbs Valley geothermal area demonstrate that the proposed method significantly reduces inversion errors while maintaining robust uncertainty assessment capabilities.

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CORAL-AUV: CFD Oriented Reinforcement Learning for Autonomous Underwater Vehicles

Jul 10, 2026

This study addresses the poor sim-to-real transfer performance of conventional autonomous underwater vehicle (AUV) control methods, which rely on oversimplified hydrodynamic models and struggle to adapt to configuration changes and environmental disturbances. To overcome this limitation, the authors propose a novel framework that integrates computational fluid dynamics (CFD) with reinforcement learning. Specifically, high-fidelity yet computationally efficient surrogate drag models (SDMs) are constructed from CFD data and embedded within a six-degree-of-freedom simulation environment to train control policies. Remarkably, the resulting policy is deployed on a physical AUV without any fine-tuning—demonstrating, for the first time, zero-shot sim-to-real transfer. Compared to controllers based on simplified models, the proposed approach reduces energy consumption by 31%, increases waypoint-to-waypoint speed by 11%, decreases trajectory error by 19%, and is the only method to successfully generalize under parameter perturbations, substantially enhancing both robustness and task performance.

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Sonar-GPS Fusion for Seabed Mapping in Turbid Shallow Waters with an Autonomous Surface Vehicle

May 03, 2026

This study addresses the challenge of high-precision, long-range seafloor mapping in turbid shallow waters, where conventional optical methods are ineffective and existing sonar systems suffer from trajectory drift. The authors propose a drift-resistant seabed mapping framework that integrates local frame alignment using forward-looking sonar with global trajectory optimization leveraging multi-sensor data (GPS, IMU, and compass). Local registration is achieved via Fourier–Mellin transform (FMT), while an extended Kalman filter refines the global trajectory. Additionally, variance-weighted image fusion is introduced to suppress stitching artifacts. Field tests conducted in an oyster farm demonstrate a 9.5% reduction in trajectory RMSE compared to a baseline FMT-only approach, achieving sub-meter reconstruction accuracy while preserving high-resolution texture—enabling precise oyster stock estimation.

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ReefMapGS: Enabling Large-Scale Underwater Reconstruction by Closing the Loop Between Multimodal SLAM and Gaussian Splatting

Apr 13, 2026

This work addresses the challenge of high computational cost in pose estimation and the difficulty of deploying underwater large-scale 3D reconstruction on field robots. The authors propose ReefMapGS, a novel framework that, for the first time, integrates multimodal SLAM with 3D Gaussian splatting in a closed-loop manner, enabling incremental reconstruction without relying on COLMAP. The method leverages acoustic, inertial, pressure, and visual sensors to construct a pose-graph SLAM system, initializes Gaussian primitives in high-confidence regions, and alternates between local image-based tracking and global Gaussian optimization. Evaluated on two complex coral reef environments, the system achieves COLMAP-free reconstruction over 700-meter AUV trajectories, significantly improving both global pose accuracy and reconstruction efficiency.

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Recent publications

Latest Papers

Metric-Based Equilibrium Selection in Noncooperative Differentiable Games

Oct 05, 2026

This study addresses the problem of selectively attracting specific equilibria in non-cooperative differentiable games without altering their equilibrium locations. To this end, it proposes a conditioning framework based on state-dependent symmetric positive-definite metrics, which achieves targeted equilibrium control by smoothly interpolating between stabilizing and destabilizing strategies. The authors demonstrate that player-independent metrics fail to preserve the stability of differential Nash equilibria, and accordingly construct a unified single metric field that retains all equilibria while prescribing their local stability types. The effectiveness of this approach is successfully validated through experiments on a continuous-commitment Stag Hunt game and an entropy-regularized Iterated Prisoner’s Dilemma.

0 citationsRead paper

The Neuro-Physical Inverter: A Modular Framework for Magnetotelluric Inversion Coupling Ensemble Conditioning with Residual Learning

Oct 02, 2026

This study addresses the challenge of simultaneously achieving uncertainty quantification and physical constraint enforcement in magnetotelluric inversion. To this end, we propose a Neural Physics Inference (NPI) framework that pioneers the coupling of an Ensemble Conditional Gaussian Process (EnsCGP) with constrained residual learning, incorporating a physics-constrained objective function to establish a modular, uncertainty-aware inversion paradigm. Crucially, this mechanism ensures the propagation of ensemble uncertainty information throughout the entire estimation process. Evaluations on both synthetic datasets and field measurements from the Gabbs Valley geothermal area demonstrate that the proposed method significantly reduces inversion errors while maintaining robust uncertainty assessment capabilities.

0 citationsRead paper

CORAL-AUV: CFD Oriented Reinforcement Learning for Autonomous Underwater Vehicles

Jul 10, 2026

This study addresses the poor sim-to-real transfer performance of conventional autonomous underwater vehicle (AUV) control methods, which rely on oversimplified hydrodynamic models and struggle to adapt to configuration changes and environmental disturbances. To overcome this limitation, the authors propose a novel framework that integrates computational fluid dynamics (CFD) with reinforcement learning. Specifically, high-fidelity yet computationally efficient surrogate drag models (SDMs) are constructed from CFD data and embedded within a six-degree-of-freedom simulation environment to train control policies. Remarkably, the resulting policy is deployed on a physical AUV without any fine-tuning—demonstrating, for the first time, zero-shot sim-to-real transfer. Compared to controllers based on simplified models, the proposed approach reduces energy consumption by 31%, increases waypoint-to-waypoint speed by 11%, decreases trajectory error by 19%, and is the only method to successfully generalize under parameter perturbations, substantially enhancing both robustness and task performance.

0 citationsRead paper

Sonar-GPS Fusion for Seabed Mapping in Turbid Shallow Waters with an Autonomous Surface Vehicle

May 03, 2026

This study addresses the challenge of high-precision, long-range seafloor mapping in turbid shallow waters, where conventional optical methods are ineffective and existing sonar systems suffer from trajectory drift. The authors propose a drift-resistant seabed mapping framework that integrates local frame alignment using forward-looking sonar with global trajectory optimization leveraging multi-sensor data (GPS, IMU, and compass). Local registration is achieved via Fourier–Mellin transform (FMT), while an extended Kalman filter refines the global trajectory. Additionally, variance-weighted image fusion is introduced to suppress stitching artifacts. Field tests conducted in an oyster farm demonstrate a 9.5% reduction in trajectory RMSE compared to a baseline FMT-only approach, achieving sub-meter reconstruction accuracy while preserving high-resolution texture—enabling precise oyster stock estimation.

0 citationsRead paper

ReefMapGS: Enabling Large-Scale Underwater Reconstruction by Closing the Loop Between Multimodal SLAM and Gaussian Splatting

Apr 13, 2026

This work addresses the challenge of high computational cost in pose estimation and the difficulty of deploying underwater large-scale 3D reconstruction on field robots. The authors propose ReefMapGS, a novel framework that, for the first time, integrates multimodal SLAM with 3D Gaussian splatting in a closed-loop manner, enabling incremental reconstruction without relying on COLMAP. The method leverages acoustic, inertial, pressure, and visual sensors to construct a pose-graph SLAM system, initializes Gaussian primitives in high-confidence regions, and alternates between local image-based tracking and global Gaussian optimization. Evaluated on two complex coral reef environments, the system achieves COLMAP-free reconstruction over 700-meter AUV trajectories, significantly improving both global pose accuracy and reconstruction efficiency.

0 citationsRead paper