Selected work

Representative Papers

A Data-Driven Framework for Unsupervised Monitoring of Transmission Systems Using End-of-Line Testing Data: A Case Study at Ford Motor Company

Oct 03, 2026

This study addresses the limitations of traditional methods in representation capacity, as well as the poor interpretability and high latency of deep learning models, for anomaly detection in high-dimensional nonlinear time series data. We propose a modular, two-stage unsupervised monitoring framework that integrates time series alignment, autoencoder-based nonlinear dimensionality reduction, and statistical control chart techniques. This approach enables Phase I process monitoring with low computational cost, overcomes predefined threshold constraints, and remains accessible to non-technical practitioners. Experimental evaluations on real-world industrial data from Ford Motor Company demonstrate that the proposed method achieves an accuracy of 0.625, a recall of 1.00, and an F1-score of 0.769, significantly outperforming existing baseline models while satisfying the dual requirements of efficiency and interpretability in industrial applications.

0 citationsRead paper

Robot Planning and Situation Handling with Active Perception

Apr 28, 2026

This work addresses the challenge of task execution failures in dynamic, open-world environments—such as those caused by jammed doors or unforeseen ground obstacles—by introducing the VAP-TAMP framework. VAP-TAMP uniquely integrates action-knowledge-guided active viewpoint selection with vision-language models and leverages scene graph construction and reasoning to enable joint task and motion planning (TAMP). The proposed approach facilitates real-time detection of and response to execution anomalies, significantly improving both task success rates and robotic autonomy in complex, dynamic settings. Evaluations on both simulated and real-world service robot platforms demonstrate its effectiveness in enhancing robustness and adaptability under uncertainty.

0 citationsRead paper

Optimal Labeler Assignment and Sampling for Active Learning in the Presence of Imperfect Labels

Dec 14, 2025

To address high label noise in active learning caused by annotator ability disparities—particularly erroneous labeling of complex instances—this paper proposes a robust, noise-resilient active learning framework. Methodologically: (1) it formulates an optimal annotator allocation model grounded in game theory, minimizing the worst-case potential noise per iteration; (2) it introduces an uncertainty-aware, noise-robust sampling strategy; and (3) it integrates multi-annotator confidence-weighted ensemble learning with noise-robust loss modeling. Extensive experiments across multiple benchmark datasets demonstrate an average 5.2% improvement in classification accuracy and a 37% reduction in label-noise sensitivity, significantly outperforming state-of-the-art active learning methods. The core contribution lies in the first unified integration of annotator capability modeling, noise-aware sampling, and robust ensemble learning within a closed-loop active learning pipeline.

0 citationsRead paper

GOMP: Grasped Object Manifold Projection for Multimodal Imitation Learning of Manipulation

Dec 03, 2025

To address trajectory inaccuracy in imitation learning caused by error accumulation during precision assembly, this paper proposes a novel imitation learning framework constrained by a low-dimensional manifold of the manipulated object. The core innovation is the first introduction of a manifold projection mechanism that constrains object motion to a task-relevant low-dimensional manifold, coupled with an *n*-armed bandit algorithm for dynamic policy adaptation—effectively suppressing error propagation without requiring additional labeled data. The method integrates multimodal perception (including tactile sensing), non-rigid object modeling, and manifold-aware optimization, yielding strong generalization capability. Evaluated on four high-precision assembly tasks, the approach achieves significant improvements in both success rate and trajectory accuracy, demonstrating its cross-modal effectiveness and engineering practicality.

0 citationsRead paper

BudgetMem: Learning Selective Memory Policies for Cost-Efficient Long-Context Processing in Language Models

Nov 07, 2025

To address the prohibitively high computational and memory overhead of large language models (LLMs) in long-context processing, this paper proposes a budget-constrained Selective Memory architecture. The method innovatively integrates a learnable memory gating mechanism with a multidimensional importance scoring function—incorporating BM25, entity density, TF-IDF, discourse markers, and positional bias—to dynamically select and retain salient information under strict memory constraints, thereby departing from the conventional full-document retrieval-augmented generation (RAG) paradigm. Evaluated on an enhanced Llama-3.2-3B-Instruct model, the approach achieves only a 1.0% F1 drop on long-document tasks while reducing memory consumption by 72.4%. Crucially, its performance advantage over baseline RAG methods grows substantially with increasing document length, demonstrating superior scalability and efficiency in resource-constrained long-context settings.

0 citationsRead paper

Recent publications

Latest Papers

A Data-Driven Framework for Unsupervised Monitoring of Transmission Systems Using End-of-Line Testing Data: A Case Study at Ford Motor Company

Oct 03, 2026

This study addresses the limitations of traditional methods in representation capacity, as well as the poor interpretability and high latency of deep learning models, for anomaly detection in high-dimensional nonlinear time series data. We propose a modular, two-stage unsupervised monitoring framework that integrates time series alignment, autoencoder-based nonlinear dimensionality reduction, and statistical control chart techniques. This approach enables Phase I process monitoring with low computational cost, overcomes predefined threshold constraints, and remains accessible to non-technical practitioners. Experimental evaluations on real-world industrial data from Ford Motor Company demonstrate that the proposed method achieves an accuracy of 0.625, a recall of 1.00, and an F1-score of 0.769, significantly outperforming existing baseline models while satisfying the dual requirements of efficiency and interpretability in industrial applications.

0 citationsRead paper

Robot Planning and Situation Handling with Active Perception

Apr 28, 2026

This work addresses the challenge of task execution failures in dynamic, open-world environments—such as those caused by jammed doors or unforeseen ground obstacles—by introducing the VAP-TAMP framework. VAP-TAMP uniquely integrates action-knowledge-guided active viewpoint selection with vision-language models and leverages scene graph construction and reasoning to enable joint task and motion planning (TAMP). The proposed approach facilitates real-time detection of and response to execution anomalies, significantly improving both task success rates and robotic autonomy in complex, dynamic settings. Evaluations on both simulated and real-world service robot platforms demonstrate its effectiveness in enhancing robustness and adaptability under uncertainty.

0 citationsRead paper

Optimal Labeler Assignment and Sampling for Active Learning in the Presence of Imperfect Labels

Dec 14, 2025

To address high label noise in active learning caused by annotator ability disparities—particularly erroneous labeling of complex instances—this paper proposes a robust, noise-resilient active learning framework. Methodologically: (1) it formulates an optimal annotator allocation model grounded in game theory, minimizing the worst-case potential noise per iteration; (2) it introduces an uncertainty-aware, noise-robust sampling strategy; and (3) it integrates multi-annotator confidence-weighted ensemble learning with noise-robust loss modeling. Extensive experiments across multiple benchmark datasets demonstrate an average 5.2% improvement in classification accuracy and a 37% reduction in label-noise sensitivity, significantly outperforming state-of-the-art active learning methods. The core contribution lies in the first unified integration of annotator capability modeling, noise-aware sampling, and robust ensemble learning within a closed-loop active learning pipeline.

0 citationsRead paper

GOMP: Grasped Object Manifold Projection for Multimodal Imitation Learning of Manipulation

Dec 03, 2025

To address trajectory inaccuracy in imitation learning caused by error accumulation during precision assembly, this paper proposes a novel imitation learning framework constrained by a low-dimensional manifold of the manipulated object. The core innovation is the first introduction of a manifold projection mechanism that constrains object motion to a task-relevant low-dimensional manifold, coupled with an *n*-armed bandit algorithm for dynamic policy adaptation—effectively suppressing error propagation without requiring additional labeled data. The method integrates multimodal perception (including tactile sensing), non-rigid object modeling, and manifold-aware optimization, yielding strong generalization capability. Evaluated on four high-precision assembly tasks, the approach achieves significant improvements in both success rate and trajectory accuracy, demonstrating its cross-modal effectiveness and engineering practicality.

0 citationsRead paper

BudgetMem: Learning Selective Memory Policies for Cost-Efficient Long-Context Processing in Language Models

Nov 07, 2025

To address the prohibitively high computational and memory overhead of large language models (LLMs) in long-context processing, this paper proposes a budget-constrained Selective Memory architecture. The method innovatively integrates a learnable memory gating mechanism with a multidimensional importance scoring function—incorporating BM25, entity density, TF-IDF, discourse markers, and positional bias—to dynamically select and retain salient information under strict memory constraints, thereby departing from the conventional full-document retrieval-augmented generation (RAG) paradigm. Evaluated on an enhanced Llama-3.2-3B-Instruct model, the approach achieves only a 1.0% F1 drop on long-document tasks while reducing memory consumption by 72.4%. Crucially, its performance advantage over baseline RAG methods grows substantially with increasing document length, demonstrating superior scalability and efficiency in resource-constrained long-context settings.

0 citationsRead paper