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Jiayuan Luo

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AAAI Conference 2025 Conference Paper

Time Series Supplier Allocation via Deep Black-Litterman Model

Xinke Jiang
Wentao Zhang
Yuchen Fang
Xiaowei Gao
Hao Chen
Haoyu Zhang
Dingyi Zhuang
Jiayuan Luo

As a typical problem of Spatiotemporal Resource Management, Time Series Supplier Allocation (TSSA) poses a complex NP-hard challenge, aimed at refining future order dispatching strategies to satisfy the trade-off between demands and maximum supply. The Black-Litterman (BL) model, which comes from financial portfolio management, offers a new perspective for the TSSA by balancing expected returns against insufficient supply risks. However, the BL model is not only constrained by manually constructed perspective matrices and spatio-temporal market dynamics but also restricted by the absence of supervisory signals and unreliable supplier data. To solve these limitations, we introduce the pioneering Deep Black-Litterman Model for TSSA, which innovatively adapts the BL model from financial domain to supply chain context. Specifically, DBLM leverages Spatio-Temporal Graph Neural Networks (STGNNs) to capture spatio-temporal dependencies for automatically generating future perspective matrices. Moreover, a novel Spearman rank correlation is designed as our DBLM supervise signal to navigate complex risks and interactions of the supplier. Finally, DBLM further uses a masking mechanism to counteract the bias of unreliable data, thus improving precision and reliability. Extensive experiments on two datasets demonstrate significant improvements of DBLM on TSSA.

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ICRA Conference 2024 Conference Paper

Grasp Manipulation Relationship Detection based on Graph Sample and Aggregation

Jiayuan Luo
Yaxin Liu
Han Wang
Mengyuan Ding
Xuguang Lan

In multi-object stacking scenarios, exploring the relationships among objects and determining the correct sequence of operations are crucial for robotic manipulation. However, previous algorithms inefficiently combine global and local information, often focusing solely on the local features of objects or the interactions of object features at a global level. This approach leads to imbalanced distribution of features and the generation of redundant or missing relationships in complex scenes, such as multi-object stacking and partial occlusion. To address this issue, we have developed a grasp manipulation relationship detection algorithm called Graph Sampling Aggregation Network for Visual Manipulation Relationship Detection (GSAGED). This algorithm assists robots in detecting targets in complex scenes and determining the appropriate grasping order. Firstly, the Positional Encoding Module in GSAGED enhances object feature information by considering global contexts. Secondly, the Graph Sampling Aggregation method effectively integrates global and local information, relieving imbalanced distribution of features. Finally, we applied the developed algorithm to a physical robot for grasping. Experimental results on the Visual Manipulation Relationship Dataset (VMRD) and the large-scale relational grasp dataset named REGRAD demonstrate that our method significantly improves the accuracy of relationship detection in complex scenes and exhibits robust generalization capabilities in real-world applications.

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