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Chang Shu

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3 papers

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ICRA Conference 2025 Conference Paper

Subassembly to Full Assembly: Effective Assembly Sequence Planning Through Graph-Based Reinforcement Learning

Chang Shu
Anton Kim
Shinkyu Park

This paper proposes an assembly sequence planning framework, named Subassembly to Assembly (S2A). The framework is designed to enable a robotic manipulator to assemble multiple parts in a prespecified structure by leveraging object manipulation actions. The primary technical challenge lies in the exponentially increasing complexity of identifying a feasible assembly sequence as the number of parts grows. To address this, we introduce a graph-based reinforcement learning approach, where a graph attention network is trained using a delayed reward assignment strategy. In this strategy, rewards are assigned only when an assembly action contributes to the successful completion of the assembly task. We validate the framework's performance through physics-based simulations, comparing it against various baselines to emphasize the significance of the proposed reward assignment approach. Additionally, we demonstrate the feasibility of deploying our framework in a real-world robotic assembly scenario.

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TCS Journal 2023 Journal Article

Edge-independent spanning trees in folded crossed cubes

Huanwen Zhang
Yan Wang
Jianxi Fan
Chang Shu

Edge-independent spanning trees (for short EISTs) have widespread applications in fault-tolerance to enhance stability and security of networks, as well as in IP fast rerouting to prevent network breakdown caused by link failure. Then, the algorithms for constructing EISTs on many classes of graphs have been investigated. The folded crossed cube was proposed based on the folded cube and the crossed cube, which possesses such appealing properties as short diameter, short mean internode distance and very low message traffic density. In this paper, we study the existence and construction of EISTs with the same root r in the n-dimensional folded crossed cube (for short F C Q n ). For v ∈ V ( F C Q n ) ∖ { r } and i ∈ { 0, 1, ⋯, n − 1 }, we first propose two algorithms to obtain the sequence S v, i and the set F v, respectively. Then, based on them, an algorithm with time complexity O ( n 2 ) by using N processors is proposed to construct n + 1 EISTs rooted at any vertex r in F C Q n, where N = 2 n. And the corresponding theoretical proof and simulation experiments are presented to verify its validity. Since F C Q n is ( n + 1 ) -regular, the result is optimal with respect to the number of EISTs constructed. Moreover, the performance of the proposed algorithm is evaluated experimentally in terms of average distance and average distance-diameter ratio of resulting EISTs.

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AAAI Conference 2020 Conference Paper

Hierarchical Knowledge Squeezed Adversarial Network Compression

Peng Li
Chang Shu
Yuan Xie
Yan Qu
Hui Kong

Deep network compression has been achieved notable progress via knowledge distillation, where a teacher-student learning manner is adopted by using predetermined loss. Recently, more focuses have been transferred to employ the adversarial training to minimize the discrepancy between distributions of output from two networks. However, they always emphasize on result-oriented learning while neglecting the scheme of process-oriented learning, leading to the loss of rich information contained in the whole network pipeline. Whereas in other (non GAN-based) process-oriented methods, the knowledge have usually been transferred in a redundant manner. Observing that, the small network can not perfectly mimic a large one due to the huge gap of network scale, we propose a knowledge transfer method, involving effective intermediate supervision, under the adversarial training framework to learn the student network. Different from the other intermediate supervision methods, we design the knowledge representation in a compact form by introducing a task-driven attention mechanism. Meanwhile, to improve the representation capability of the attention-based method, a hierarchical structure is utilized so that powerful but highly squeezed knowledge is realized and the knowledge from teacher network could accommodate the size of student network. Extensive experimental results on three typical benchmark datasets, i. e. , CIFAR-10, CIFAR-100, and ImageNet, demonstrate that our method achieves highly superior performances against state-of-the-art methods.

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