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Xudong Pan

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4 papers

2 author rows

EAAI Journal 2025 Journal Article

Planning scheme of artificial assembly posture and arm movement path in narrow space

Yizhen Zheng
Yuefeng Li
Xudong Pan
Fanwei Meng
Changyu Chen

Manual assembly in a narrow space involves problems of low efficiency and difficult assembly. In view of the lack of assembly process planning and assisted manual assembly in this kind of scenario, a hybrid modeling simulation method of human posture was proposed. This method combined the characteristics of manual assembly in narrow space. The assembly planning process was divided into two parts: trunk and lower limb posture planning and human arm movement planning, to reduce the complexity of planning and the difficulty of manual assembly. In the posture planning part, this study solved for the human trunk and lower limbs by establishing a multi-objective optimization model and achieved automatic screening of assembly posture according to the weight of each target element. Arm movement planning involved a neural network of assembly spaces to guide the sampling process of the path planner combined with the inverse solution of arm kinematics for environmental collision detection to quickly obtain a feasible collision-free arm movement path from the initial position to the assembly target. Finally, the feasibility of the method in a narrow space was verified by building a scene and carrying out the corresponding manual assembly operation experiments.

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NeurIPS Conference 2022 Conference Paper

House of Cans: Covert Transmission of Internal Datasets via Capacity-Aware Neuron Steganography

Xudong Pan
Shengyao Zhang
Mi Zhang
Yifan Yan
Min Yang

In this paper, we present a capacity-aware neuron steganography scheme (i. e. , Cans) to covertly transmit multiple private machine learning (ML) datasets via a scheduled-to-publish deep neural network (DNN) as the carrier model. Unlike existing steganography schemes which treat the DNN parameters as bit strings, \textit{Cans} for the first time exploits the learning capacity of the carrier model via a novel parameter sharing mechanism. Extensive evaluation shows, Cans is the first working scheme which can covertly transmit over $10000$ real-world data samples within a carrier model which has $220\times$ less parameters than the total size of the stolen data, and simultaneously transmit multiple heterogeneous datasets within a single carrier model, under a trivial distortion rate ($<10^{-5}$) and with almost no utility loss on the carrier model ($<1\%$). Besides, Cans implements by-design redundancy to be resilient against common post-processing techniques on the carrier model before the publishing.

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AAAI Conference 2020 Conference Paper

Improving the Robustness of Wasserstein Embedding by Adversarial PAC-Bayesian Learning

Daizong Ding
Mi Zhang
Xudong Pan
Min Yang
Xiangnan He

Node embedding is a crucial task in graph analysis. Recently, several methods are proposed to embed a node as a distribution rather than a vector to capture more information. Although these methods achieved noticeable improvements, their extra complexity brings new challenges. For example, the learned representations of nodes could be sensitive to external noises on the graph and vulnerable to adversarial behaviors. In this paper, we ﬁrst derive an upper bound on generalization error for Wasserstein embedding via the PAC- Bayesian theory. Based on this, we propose an algorithm called Adversarial PAC-Bayesian Learning (APBL) in order to minimize the generalization error bound. Furthermore, we provide a model called Regularized Adversarial Wasserstein Embedding Network (RAWEN) as an implementation of APBL. Besides our comprehensive analysis of the robustness of RAWEN, our work for the ﬁrst time explores more kinds of embedded distributions. For evaluations, we conduct extensive experiments to demonstrate the effectiveness and robustness of our proposed embedding model compared with the state-of-the-art methods.

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ICML Conference 2018 Conference Paper

Theoretical Analysis of Image-to-Image Translation with Adversarial Learning

Xudong Pan
Mi Zhang 0001
Daizong Ding

Recently, a unified model for image-to-image translation tasks within adversarial learning framework has aroused widespread research interests in computer vision practitioners. Their reported empirical success however lacks solid theoretical interpretations for its inherent mechanism. In this paper, we reformulate their model from a brand-new geometrical perspective and have eventually reached a full interpretation on some interesting but unclear empirical phenomenons from their experiments. Furthermore, by extending the definition of generalization for generative adversarial nets to a broader sense, we have derived a condition to control the generalization capability of their model. According to our derived condition, several practical suggestions have also been proposed on model design and dataset construction as a guidance for further empirical researches.

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