Arrow Research search

Author name cluster

Yue Chen

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

15 papers
2 author rows

Possible papers

15

AAAI Conference 2026 Conference Paper

A3D: Adaptive Affordance Assembly with Dual-Arm Manipulation

  • Jiaqi Liang
  • Yue Chen
  • Qize Yu
  • Yan Shen
  • Haipeng Zhang
  • Hao Dong
  • Ruihai Wu

Furniture assembly is a crucial yet challenging task for robots, requiring precise dual-arm coordination where one arm manipulates parts while the other provides collaborative support and stabilization. To accomplish this task more effectively, robots need to actively adapt support strategies throughout the long-horizon assembly process, while also generalizing across diverse part geometries. We propose A3D, a framework which learns adaptive affordances to identify optimal support and stabilization locations on furniture parts. The method employs dense point-level geometric representations to model part interaction patterns, enabling generalization across varied geometries. To handle evolving assembly states, we introduce an adaptive module that uses interaction feedback to dynamically adjust support strategies during assembly based on previous interactions. We establish a simulation environment featuring 50 diverse parts across 8 furniture types, designed for dual-arm collaboration evaluation. Experiments demonstrate that our framework generalizes effectively to diverse part geometries and furniture categories in both simulation and real-world settings.

PDF Details DOI

EAAI Journal 2026 Journal Article

Damage assessment of thermal-humidity-mechanical coupling field of early-age concrete based on adaptive physics informed neural network

  • Shiqi Wang
  • Yue Chen
  • Jinlong Liu
  • Fangzhou Lin
  • Lei Xu

The crack-damage resistance of early-age concrete is affected by multiple factors such as hydration, self-drying, temperature and humidity diffusion, and material properties, which are difficult to be accurately evaluated by traditional theories and numerical models. This paper proposed an adaptive physics-informed back propagation neural network (BPINN) to accurately evaluate the damage of early-age concrete under multi-physics field coupling. The temperature and humidity diffusion and shrinkage models are used as physics loss functions to guide the model in learning the physics laws. Furthermore, time-dependent factor weights are constructed for both the physics and boundary equations to enhance the model's ability to learn the spatiotemporal feature distribution of the sampling points. BPINN effectively simulates the influence of concrete strength grade and boundary conditions on temperature and humidity diffusion, with the average error less than 5 %. The LOSS differences of traditional physics informed neural network (PINN) and BPINN in time step, activation function, hidden layer and neuron number are quantified. Compared with the traditional PINN, the LOSS of BPINN is reduced by 62. 4 %. On this basis, the predictive performance of BPINN and four types of data-driven models is compared to verify the influence of physics constraint, as BPINN has the smallest statistical loss and data discreteness. The model proposed in this paper enhances the learning ability of spatial-temporal features by balancing the weight between boundary and physics equations, providing new insights for the thermo-hygro-mechanical coupling field in early-age concrete.

Details DOI

AAAI Conference 2026 Conference Paper

FedCure: Mitigating Participation Bias in Semi-Asynchronous Federated Learning with Non-IID Data

  • Yue Chen
  • Jianfeng Lu
  • Shuqin Cao
  • Wei Wang
  • Gang Li
  • Guanghui Wen

While semi-asynchronous federated learning (SAFL) combines the efficiency of synchronous training with the flexibility of asynchronous updates, it inherently suffers from participation bias, which is further exacerbated by non-IID data distributions. More importantly, hierarchical architecture shifts participation from individual clients to client groups, thereby further intensifying this issue. Despite notable advancements in SAFL research, most existing works still focus on conventional cloud-end architectures while largely overlooking the critical impact of non-IID data on scheduling across the cloud–edge–client hierarchy. To tackle these challenges, we propose FedCure, an innovative semiasynchronous Federated learning framework that leverages Coalition construction and participation-aware scheduling to mitigate participation bias with non-IID data. Specifically, FedCure operates through three key rules: (1) a preference rule that optimizes coalition formation by maximizing collective benefits and establishing theoretically stable partitions to reduce non-IID-induced performance degradation; (2) a scheduling rule that integrates the virtual queue technique with Bayesian-estimated coalition dynamics, mitigating efficiency loss while ensuring mean rate stability; and (3) a resource allocation rule that enhances computational efficiency by optimizing client CPU frequencies based on estimated coalition dynamics while satisfying delay requirements. Comprehensive experiments on four real-world datasets demonstrate that FedCure improves accuracy by up to 5.1x compared with four state-of-the-art baselines, while significantly enhancing efficiency with the lowest coefficient of variation 0.0223 for per-round latency and maintaining long-term balance across diverse scenarios.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

DexGarmentLab: Dexterous Garment Manipulation Environment with Generalizable Policy

  • Yuran Wang
  • Ruihai Wu
  • Yue Chen
  • Jiarui Wang
  • Jiaqi Liang
  • Ziyu Zhu
  • Haoran Geng
  • Jitendra Malik

Garment manipulation is a critical challenge due to the diversity in garment categories, geometries, and deformations. Despite this, humans can effortlessly handle garments, thanks to the dexterity of our hands. However, existing research in the field has struggled to replicate this level of dexterity, primarily hindered by the lack of realistic simulations of dexterous garment manipulation. Therefore, we propose DexGarmentLab, the first environment specifically designed for dexterous (especially bimanual) garment manipulation, which features large-scale high-quality 3D assets for 15 task scenarios, and refines simulation techniques tailored for garment modeling to reduce the sim-to-real gap. Previous data collection typically relies on teleoperation or training expert reinforcement learning (RL) policies, which are labor-intensive and inefficient. In this paper, we leverage garment structural correspondence to automatically generate a dataset with diverse trajectories using only a single expert demonstration, significantly reducing manual intervention. However, even extensive demonstrations cannot cover the infinite states of garments, which necessitates the exploration of new algorithms. To improve generalization across diverse garment shapes and deformations, we propose a Hierarchical gArment-manipuLation pOlicy (HALO). It first identifies transferable affordance points to accurately locate the manipulation area, then generates generalizable trajectories to complete the task. Through extensive experiments and detailed analysis of our method and baseline, we demonstrate that HALO consistently outperforms existing methods, successfully generalizing to previously unseen instances even with significant variations in shape and deformation where others fail. Our project page is available at: https: //wayrise. github. io/DexGarmentLab/.

PDF Details

ICLR Conference 2025 Conference Paper

Et-Seed: Efficient trajectory-Level SE(3) equivariant diffusion Policy

  • Chenrui Tie
  • Yue Chen
  • Ruihai Wu
  • Boxuan Dong
  • Zeyi Li
  • Chongkai Gao
  • Hao Dong 0003

Imitation learning, e.g., diffusion policy, has been proven effective in various robotic manipulation tasks. However, extensive demonstrations are required for policy robustness and generalization. To reduce the demonstration reliance, we leverage spatial symmetry and propose ET-SEED, an efficient trajectory-level SE(3) equivariant diffusion model for generating action sequences in complex robot manipulation tasks. Further, previous equivariant diffusion models require the per-step equivariance in the Markov process, making it difficult to learn policy under such strong constraints. We theoretically extend equivariant Markov kernels and simplify the condition of equivariant diffusion process, thereby significantly improving training efficiency for trajectory-level SE(3) equivariant diffusion policy in an end-to-end manner. We evaluate ET-SEED on representative robotic manipulation tasks, involving rigid body, articulated and deformable object. Experiments demonstrate superior data efficiency and manipulation proficiency of our proposed method, as well as its ability to generalize to unseen configurations with only a few demonstrations. Website: https://et-seed.github.io/

Details

EAAI Journal 2025 Journal Article

Multi-step control method of traffic flow data quality based on spatiotemporal similarity at video frame rate

  • Yue Chen
  • Jian Lu

Traffic flow data is a quantitative description of traffic operation status. It lays a data foundation for optimizing traffic management, improving travel efficiency and ensuring traffic safety. Traffic flow data quality is crucial to the construction and operation of intelligent transportation system. At present, traffic flow data mostly come from vehicle detectors, but its long sampling period and sparse sampling points affect the quality control effect of traffic flow data. To solve these problems, we propose a multi-step automatic control method of traffic flow data quality based on spatiotemporal similarity. In this framework, we divide the data quality control process into preliminary repair and combinatorial optimization by combining the spatiotemporal advantages of the cross-sectional traffic flow data at video frame rate. Firstly, according to the different characteristics of the missing data, the preliminary repair is carried out automatically. For the missing data at the section level, the combined repair method based on the preprocessing of continuous missing data and the segmented low-order interpolation is proposed. For the missing data at road network level, the adaptive weight exponential smoothing method based on the time similarity is proposed. Then, a multi-sectional optimization model of based on spatiotemporal similarity was constructed to further optimize the preliminary repair results. The experimental results show that the preliminary repair method proposed in this paper is superior to other baseline models, and the combined optimization model based on the preliminary repair data greatly improves the data repair effect, which is suitable for different missing rates and types, and has certain competitiveness in traffic flow data quality control.

Details DOI

NeurIPS Conference 2025 Conference Paper

Results of the Big ANN: NeurIPS’23 competition

  • Harsha Vardhan simhadri
  • Martin Aumüller
  • Matthijs Douze
  • Dmitry Baranchuk
  • Amir Ingber
  • Edo Liberty
  • George Williams
  • Ben Landrum

The 2023 Big ANN Challenge, held at NeurIPS 2023, focused on advancing the state-of-the-art in indexing data structures and search algorithms for practical variants of Approximate Nearest Neighbor (ANN) search that reflect its the growing complexity and diversity of workloads. Unlike prior challenges that emphasized scaling up classical ANN search (Simhadri et al. , NeurIPS 2021), this competition addressed sparse, filtered, out-of-distribution, and streaming variants of ANNS. Participants developed and submitted innovative solutions that were evaluated on new standard datasets with constrained computational resources. The results showcased significant improvements in search accuracy and efficiency, with notable contributions from both academic and industrial teams. This paper summarizes the competition tracks, datasets, evaluation metrics, and the innovative approaches of the top-performing submissions, providing insights into the current advancements and future directions in the field of approximate nearest neighbor search.

PDF Details

IJCAI Conference 2024 Conference Paper

LEAP: Optimization Hierarchical Federated Learning on Non-IID Data with Coalition Formation Game

  • Jianfeng Lu
  • Yue Chen
  • Shuqin Cao
  • Longbiao Chen
  • Wei Wang
  • Yun Xin

Although Hierarchical Federated Learning (HFL) utilizes edge servers (ESs) to alleviate communication burdens, its model performance will be degraded by non-IID data and limited communication resources. Current works often assume that data is uniformly distributed, which however contradicts the heterogeneity of IoT. Solutions involving additional model training to check the data distribution inevitably increase computational costs and the risk of privacy leakage. The challenges in solving these issues are how to reduce the impact of non-IID data without involving raw data, and how to rationalize the communication resource allocation for addressing straggler problem. To tackle these challenges, we propose a novel optimization method based on coaLition formation gamE and grAdient Projection, called LEAP. Specifically, we combine edge data distribution with coalition formation game innovatively to adjust the correlations between clients and ESs dynamically, ensuring optimal correlations. We further capture the client heterogeneity to achieve the rational bandwidth allocation from coalition perception and determine the optimal transmission power within specified delay constraints at the client level. Experimental results on four real datasets show that LEAP is able to achieve 20. 62% improvement in model accuracy compared to the state-of-the-art baselines. Moreover, LEAP effectively reduces transmission energy consumption by at least about 2. 24 times.

PDF Details DOI

IJCAI Conference 2024 Conference Paper

MuEP: A Multimodal Benchmark for Embodied Planning with Foundation Models

  • Kanxue Li
  • Baosheng Yu
  • Qi Zheng
  • Yibing Zhan
  • Yuhui Zhang
  • Tianle Zhang
  • Yijun Yang
  • Yue Chen

Foundation models have demonstrated significant emergent abilities, holding great promise for enhancing embodied agents' reasoning and planning capacities. However, the absence of a comprehensive benchmark for evaluating embodied agents with multimodal observations in complex environments remains a notable gap. In this paper, we present MuEP, a comprehensive Multimodal benchmark for Embodied Planning. MuEP facilitates the evaluation of multimodal and multi-turn interactions of embodied agents in complex scenes, incorporating fine-grained evaluation metrics that provide insights into the performance of embodied agents throughout each task. Furthermore, we evaluate embodied agents with recent state-of-the-art foundation models, including large language models (LLMs) and large multimodal models (LMMs), on the proposed benchmark. Experimental results show that foundation models based on textual representations of environments usually outperform their visual counterparts, suggesting a gap in embodied planning abilities with multimodal observations. We also find that control language generation is an indispensable ability beyond common-sense knowledge for accurate embodied task completion. We hope the proposed MuEP benchmark can contribute to the advancement of embodied AI with foundation models.

PDF Details DOI

ICRA Conference 2024 Conference Paper

Quasi-static Path Planning for Continuum Robots By Sampling on Implicit Manifold

  • Yifan Wang
  • Yue Chen

Continuum robots (CR) offer excellent dexterity and compliance in contrast to rigid-link robots, making them suitable for navigating through, and interacting with, confined environments. However, the study of path planning for CRs while considering external elastic contact is limited. The challenge lies in the fact that CRs can have multiple possible configurations when in contact, rendering the forward kinematics not well-defined, and characterizing the set of feasible robot configurations is non-trivial. In this paper, we propose to perform quasi-static path planning on an implicit manifold. We model elastic obstacles as external potential fields and formulate the robot statics in the potential field as the extremal trajectory of an optimal control problem. We show that the set of stable robot configurations is a smooth manifold diffeomorphic to a submanifold embedded in the product space of the CR actuation and base internal wrench. We then propose to perform path planning on this manifold using AtlasRRT*, a sampling-based planner dedicated to planning on implicit manifolds. Simulations in different operation scenarios were conducted and the results show that the proposed planner outperforms Euclidean space planners in terms of success rate and computational efficiency.

Details

ICRA Conference 2023 Conference Paper

Place Recognition under Occlusion and Changing Appearance via Disentangled Representations

  • Yue Chen
  • Xingyu Chen
  • Yicen Li

Place recognition is a critical and challenging task for mobile robots, aiming to retrieve an image captured at the same place as a query image from a database. Existing methods tend to fail while robots move autonomously under occlusion (e. g. , car, bus, truck) and changing appearance (e. g. , illumination changes, seasonal variation). Because they encode the image into only one code, entangling place features with appearance and occlusion features. To overcome this limitation, we propose PROCA, an unsupervised approach to decompose the image representation into three codes: a place code used as a descriptor to retrieve images, an appearance code that captures appearance properties, and an occlusion code that encodes occlusion content. Extensive experiments show that our model outperforms the state-of-the-art methods. Our code and data are available at https://github.com/rover-xingyu/PROCA.

Details

EAAI Journal 2023 Journal Article

SReResNet: A stage recursive residual network for suppressing semantic redundancy during feature extraction

  • Chaojun Lin
  • Ying Shi
  • Changjun Xie
  • Yue Chen

Feature extraction neural networks are essential components of computer vision systems. As the most famous one, ResNet has been widely used in engineering. Although many modern networks have outperformed ResNet, the costly pretraining and hyper-parameter optimization processes prevent their application of them in industrial computer vision systems. To avoid these costly processes, a Stage Recursive Residual Network (SReResNet) is proposed, which merely adjusts the forward propagation pipeline without changing the parameter architecture of ResNet. Thus, it can inherit existing model parameters of ResNet and replace ResNet through simple fine-tuning. Moreover, SReResNet is the first network to improve accuracy by utilizing the semantic trend during feature extraction instead of well-designed modules. It models a series of cascaded modules as a semantic unit and feeds the high-level feature maps back to the low-level modules for further semantic redundancy suppression through one feedback connection within the unit based on the semantic trend and the mechanism of looking and thinking twice. For object detection tasks, a Stage Recursive Feature Pyramid Network (SReFPN) is also proposed to rethink and suppress semantic redundancy further. Experiments demonstrate that SReResNet outperforms its counterparts in object detection and image classification tasks. On the MS COCO 2017 dataset, SReResNet outperforms ResNet with 1. 2 Box AP improvement, and SReResNet with SReFPN further achieves 2. 5 Box AP improvement without bells and whistles. On the CIFAR-100 dataset, SReResNet outperforms its counterparts, such as ResNet, DenseNet, and ConvNeXt, with at least 2. 33% top-1 acc improvement. The code is available at https: //github. com/unbelieboomboom/SReResNet.

Details DOI

ICRA Conference 2023 Conference Paper

Tendon-Driven Soft Robotic Gripper with Integrated Ripeness Sensing for Blackberry Harvesting

  • Alex S. Qiu
  • Claire Young
  • Anthony L. Gunderman
  • Milad Azizkhani
  • Yue Chen
  • Ai-Ping Hu

Growing global demand for food, coupled with continuing labor shortages, motivate the need for automated agricultural harvesting. While some specialty crops (e. g. , apples, peaches, blueberries) can be harvested via existing harvesting modalities, fruits such as blackberries and raspberries require delicate handling to mitigate fruit damage that could significantly impact marketability. This motivates the development of soft robotic solutions that enable efficient, delicate harvesting. This paper presents the design, fabrication, and feasibility testing of a tendon-driven soft gripping system focused on blackberries, which are a fragile fruit susceptible to post-harvest damage. The gripper is low-cost and small form factor, allowing for the integration of a micro-servo for tendon retraction, a near-infrared (NIR) based blackberry ripeness sensor utilizing the reflectance modality for identifying fully ripe blackberries, and an endoscopic camera for visual servoing. The gripper was used to harvest 139 berries with manual positioning in two separate field tests. Field testing found an average retention force of 2. 06 N and 6. 08 N for ripe and unripe blackberries, respectively. Sensor tests identified an average reflectance of 16. 78 and 21. 70 for ripe and unripe blackberries, respectively, indicating a clear distinction between the two ripeness levels. Finally, the soft robotic gripper was integrated onto a UR5 robot arm and successfully harvested fifteen artificial blackberries in a lab setting using visual servoing.

Details

YNIMG Journal 2019 Journal Article

Characterization of lenticulostriate arteries with high resolution black-blood T1-weighted turbo spin echo with variable flip angles at 3 and 7 Tesla

  • Samantha J. Ma
  • Mona Sharifi Sarabi
  • Lirong Yan
  • Xingfeng Shao
  • Yue Chen
  • Qi Yang
  • Kay Jann
  • Arthur W. Toga

Objectives The lenticulostriate arteries (LSAs) with small diameters of a few hundred microns take origin directly from the high flow middle cerebral artery (MCA), making them especially susceptible to damage (e. g. by hypertension). This study aims to present high resolution (isotropic ∼0. 5 mm), black blood MRI for the visualization and characterization of LSAs at both 3 T and 7 T. Materials and methods T1-weighted 3D turbo spin-echo with variable flip angles (T1w TSE-VFA) sequences were optimized for the visualization of LSAs by performing extended phase graph (EPG) simulations. Twenty healthy volunteers (15 under 35 years old, 5 over 60 years old) were imaged with the T1w TSE-VFA sequences at both 3 T and 7 T. Contrast-to-noise ratio (CNR) was quantified, and LSAs were manually segmented using ITK-SNAP. Automated Reeb graph shape analysis was performed to extract features including vessel length and tortuosity. All quantitative metrics were compared between the two field strengths and two age groups using ANOVA. Results LSAs can be clearly delineated using optimized 3D T1w TSE-VFA at 3 T and 7 T, and a greater number of LSA branches can be detected compared to those by time-of-flight MR angiography (TOF MRA) at 7 T. The CNR of LSAs was comparable between 7 T and 3 T. T1w TSE-VFA showed significantly higher CNR than TOF MRA at the stem portion of the LSAs branching off the medial middle cerebral artery. The mean vessel length and tortuosity were greater on TOF MRA compared to TSE-VFA. The number of detected LSAs by both TSE-VFA and TOF MRA was significantly reduced in aged subjects, while the mean vessel length measured on 7 T TSE-VFA showed significant difference between the two age groups. Conclusion The high-resolution black-blood 3D T1w TSE-VFA sequence offers a new method for the visualization and quantification of LSAs at both 3 T and 7 T, which may be applied for a number of pathological conditions related to the damage of LSAs.

Details DOI

AAAI Conference 2012 Conference Paper

The Complexity of Planning Revisited — A Parameterized Analysis

  • Christer Bäckström
  • Yue Chen
  • Peter Jonsson
  • Sebastian Ordyniak
  • Stefan Szeider

The early classifications of the computational complexity of planning under various restrictions in STRIPS (Bylander) and SAS+ (Bäckström and Nebel) have influenced following research in planning in many ways. We go back and reanalyse their subclasses, but this time using the more modern tool of parameterized complexity analysis. This provides new results that together with the old results give a more detailed picture of the complexity landscape. We demonstrate separation results not possible with standard complexity theory, which contributes to explaining why certain cases of planning have seemed simpler in practice than theory has predicted. In particular, we show that certain restrictions of practical interest are tractable in the parameterized sense of the term, and that a simple heuristic is sufficient to make a well-known partialorder planner exploit this fact.

PDF Details