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Hourglass-GCN for 3D Human Pose Estimation Using Skeleton Structure and View Correlation

Computers, Materials & Continua 2025年第1期82卷 173-191页

作者： Ange Chen Chengdong Wu Chuanjiang Leng Faculty of Robot Science and Engineering Northeastern UniversityShenyang110169China

Previous multi-view 3D human pose estimation methods neither correlate different human joints in each view nor model learnable correlations between the same joints in different views explicitly,meaning that skeleton structure information is not utilized and multi-view pose information is not completely ***,existing graph convolutional operations do not consider the specificity of different joints and different views of pose information when processing skeleton graphs,making the correlation weights between nodes in the graph and their neighborhood nodes *** Graph Convolutional Networks(GCNs)cannot extract global and deeplevel skeleton structure information and view correlations *** solve these problems,pre-estimated multiview 2D poses are designed as a multi-view skeleton graph to fuse skeleton priors and view correlations explicitly to process occlusion problem,with the skeleton-edge and symmetry-edge representing the structure correlations between adjacent joints in each viewof skeleton graph and the view-edge representing the view correlations between the same joints in different *** make graph convolution operation mine elaborate and sufficient skeleton structure information and view correlations,different correlation weights are assigned to different categories of neighborhood nodes and further assigned to each node in the *** on the graph convolution operation proposed above,a Residual Graph Convolution(RGC)module is designed as the basic module to be combined with the simplified Hourglass architecture to construct the Hourglass-GCN as our 3D pose estimation ***-GCNwith a symmetrical and concise architecture processes three scales ofmulti-viewskeleton graphs to extract local-to-global scale and shallow-to-deep level skeleton features *** results on common large 3D pose dataset Human3.6M and MPI-INF-3DHP show that Hourglass-GCN outperforms some excellent methods in 3D pose estimati

关键词： 3D human pose estimation multi-view skeleton graph elaborate graph convolution operation Hourglass-GCN

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A Novel Robust Adaptive Impedance Control Scheme for Contact Force Tracking With Unknown Environment

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IEEE robotics and Automation Letters 2025年第7期10卷 6744-6751页

作者： Song, Tangzhong Fang, Lijin Qian, Yian Northeastern University Faculty of Robot Science and Engineering Shenyang China

Tracking desired force on various environmental surfaces is essential in many modern robotic applications. The constant impedance control (CIPC) has ability to realize this goal with clear environmental information (including stiffness and geometry), but failed when the information is not available. The traditional adaptive impedance control (TAIPC) is capable of tracking desired force even when the stiffness and geometry of environment are unknown, but large force overshoot is caused in contact phase. To cope with drawbacks existed in above two methods, a novel brief and effective robust adaptive impedance control scheme is proposed in this work, which has good ability for desired force tracking with unknown environmental information. A new adaptive compensator is introduced into the impedance framework to deal with lumped uncertainties caused by unknown stiffness and geometry of environment. It can minimize force tracking error and the force overshoot has also been reduced a lot. The stability and convergence analysis is conducted using a spring model, but the method is not applicable to a complex second-order environmental model. Simulation and experimental results sufficiently demonstrate the effectiveness and confirm the theoretical claims of the method. © 2016 IEEE.

关键词： robot applications

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Brain Cognition Mechanism-Inspired Hierarchical Navigation Method for Mobile robots

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Journal of Bionic engineering 2024年第2期21卷 852-865页

作者： Qiang Zou Chengdong Wu Ming Cong Dong Liu Faculty of Robot Science and Engineering Northeastern UniversityShenyang110819China School of Mechanical Engineering Dalian University of TechnologyDalian116024China

Autonomous navigation is a fundamental problem in *** methods generally build point cloud map or dense feature map in perceptual space;due to lack of cognition and memory formation mechanism,traditional methods exist poor robustness and low cognitive *** a new navigation technology that draws inspiration from mammal’s navigation,bionic navigation method can map perceptual information into cognitive space,and have strong autonomy and environment *** improve the robot’s autonomous navigation ability,this paper proposes a cognitive map-based hierarchical navigation ***,the mammals’navigation-related grid cells and head direction cells are modeled to provide the robots with location *** then a global path planning strategy based on cognitive map is proposed,which can anticipate one preferred global path to the target with high efficiency and short ***,a hierarchical motion controlling method is proposed,with which the target navigation can be divided into several sub-target navigation,and the mobile robot can reach to these sub-targets with high confidence ***,some experiments are implemented,the results show that the proposed path planning method can avoid passing through obstacles and obtain one preferred global path to the target with high efficiency,and the time cost does not increase extremely with the increase of experience nodes *** motion controlling results show that the mobile robot can arrive at the target successfully only depending on its self-motion information,which is an effective attempt and reflects strong bionic properties.

关键词： Bionic navigation Spatial localization cells Global path planning Hierarchical motion controlling

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Skeleton-Based Action Recognition with Adaptive Connections and Motion Modeling 6

Skeleton-Based Action Recognition with Adaptive Connections ...

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6th International Conference on Industrial Artificial Intelligence, IAI 2024

作者： Geng, Qizhao Zheng, Chao Li, Ziao Wang, Junyi Northeastern University Faculty of Robot Science and Engineering Shenyang China Northeastern University Faculty of Robot Science and Engineering and Foshan Graduate School of Innovation China

ISBN: (纸本)9798350356618

In the field of skeleton-based action recognition, ST-GCN has gained popularity because of its strong capability to model graph data and its effective use of prior knowledge regarding human joint connections. However, it faces certain challenges and limitations, such as difficulties in transferring information between non-adjacent joints and dealing with subtle variations in action speed and acceleration. To address these issues, we present a Graph Convolution Network based Adaptive connections and Motion modeling (AM-GCN). Firstly, we remove the constraints of skeleton graphs by using learnable adjacency matrices to represent dynamic connections between joints, resulting in information exchange between non-adjacent joints. Additionally, we propose a joint motion modeling technique that combines position, velocity, and acceleration information to provide richer prior knowledge about human movements, further enhancing the model's performance. Extensive experiments conducted on the NTU-RGB+D dataset show that our method performs well in skeleton-based human behavior analysis tasks, demonstrating its potential and value in practical applications. © 2024 IEEE.

关键词： Knowledge graph

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Clinical Knowledge-Based Hybrid Swin Transformer for Brain Tumor Segmentation

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Computers, Materials & Continua 2023年第9期76卷 3797-3811页

作者： Xiaoliang Lei Xiaosheng Yu Hao Wu Chengdong Wu Jingsi Zhang College of Information Science and Engineering Northeastern UniversityShenyang110819China Faculty of Robot Science and Engineer Northeastern UniversityShenyang110819China Faculty of Engineering Macquarie UniversitySydneyNSW2109Australia

Accurate tumor segmentation from brain tissues in Magnetic Resonance Imaging(MRI)imaging is crucial in the pre-surgical planning of brain tumor *** images’heterogeneous intensity and fuzzy boundaries make brain tumor segmentation ***,recent studies have yet to fully employ MRI sequences’considerable and supplementary information,which offers critical a priori *** paper proposes a clinical knowledge-based hybrid Swin Transformermultimodal brain tumor segmentation algorithmbased on how experts identify malignancies from MRI *** the encoder phase,a dual backbone network with a Swin Transformer backbone to capture long dependencies from 3D MR images and a Convolutional Neural Network(CNN)-based backbone to represent local features have been *** of directly connecting all the MRI sequences,the proposed method re-organizes them and splits them into two groups based on MRI principles and characteristics:T1 and T1ce,T2 and *** aggregated images are received by the dual-stem Swin Transformer-based encoder branch,and the multimodal sequence-interacted cross-attention module(MScAM)captures the interactive information between two sets of linked modalities in each *** the CNN-based encoder branch,a triple down-sampling module(TDsM)has been proposed to balance the performance while *** the final stage of the encoder,the feature maps acquired from two branches are concatenated as input to the decoder,which is constrained by MScAM *** proposed method has been evaluated on datasets from the MICCAI BraTS2021 *** results of the experiments demonstrate that the method algorithm can precisely segment brain tumors,especially the portions within tumors.

关键词： Brain tumor segmentation swin transformer multimodal clinical knowledge

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Middle Code Prediction: Enhancing Code Generation for Uncommon Programming Languages in robotics 16

Middle Code Prediction: Enhancing Code Generation for Uncomm...

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16th Asian Conference on Machine Learning, ACML 2024

作者： Jia, Zixi Gao, Hongbin Li, Hexiao Faculty of Robot Science and Engineering Northeastern University Shenyang China

Generating executable code through natural language instructions to drive robotic movements is considered a crucial step towards achieving embodied intelligence. However, in the robotics domain, the scarcity of programming language data often necessitates manually encapsulating high-level APIs to enable Large Language Models(LLMs) to predict code correctly, which is time-consuming and incomplete. Therefore, this paper proposes a three-stage Middle Code Prediction(MCP) scheme, by injecting appropriate prompts at different stages, the LLMs can shift towards predicting middle code that it understands more easily. This middle code can then be converted into the final code through specific scripts, accomplishing the task of generating code in uncommon programming languages automatically and without the need for manually encapsulating high-level APIs. We tested our approach on Hospital Item Transport Dataset(HITD) and found that MCP could improve the mean accuracy of various baseline models to varying degrees, with an overall increase of 31%, while also enhancing the noise resistance of fine-tuned models. We conducted real-world experiments on industrial robotic arms, verifying the feasibility of MCP in scenarios with no API and partial API encapsulation. The method proposed in this paper provides a guideline for code generation in uncommon programming languages within the context of LLMs. Our experimental dataset is available at https://***/Ghbbbbb/MCP. © 2024 Z. Jia, H. Gao & H. Li.

关键词： robot programming

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DCoT: Dual Chain-of-Thought Prompting for Large Multimodal Models 16

DCoT: Dual Chain-of-Thought Prompting for Large Multimodal M...

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16th Asian Conference on Machine Learning, ACML 2024

作者： Jia, Zixi Liu, Jiqiang Li, Hexiao Liu, Qinghua Gao, Hongbin Faculty of Robot Science and Engineering Northeastern University Shenyang China

Inference augmentation techniques such as Chain-of-Thought have already made their mark in Large Language Models (LLMs). However, transferring these advances to Large Multimodal Models (LMMs) presents greater challenges. Drawing inspiration from human cognitive processes, this paper proposes a plug-and-play Dual Chain-of-Thought strategy, a novel pipeline that combines visual and textual guidance to improve the performance of LMMs in complex multimodal tasks. The DCoT strategy uses a dual guidance mechanism to use bounding box markers to guide the model’s attention to the image region related to the query problem in the visual aspect, so as to achieve fine-grained image guidance, and in the text aspect, we propose a Fast In-Context Retrieval Framework (FICRF) dynamically and automatically obtains the most suitable examples from the well-built demonstration example cluster as context guidance according to the current problem. This bimodal approach that utilizes visual and textual guidance enhances the inference capabilities of LMMs. Extensive experiments on different LMMs and benchmark datasets have validated its effectiveness, opening up a new path in multimodal inference. Showcasing how the synergistic combination of visual and textual instructions can take the performance of these models to new heights, while demonstrating the potential of Chain-of-Thought and In-Context Learning as a superior alternative to the fine-tuning of LMMs. © 2024 Z. Jia, J. Liu, H. Li, Q. Liu & H. Gao.

关键词： In-Context Learning Large Multimodal Models Multimodal Chain-of-Thought

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Leveraging Frequency and Spatial Domain Information for Underwater Image Restoration

Leveraging Frequency and Spatial Domain Information for Unde...

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2024 International Conference on Intelligent Systems and robotics, CISR 2024

作者： Zhang, Haopeng Xu, Hongli Yu, Xiaosheng Zhang, Xiangyue Wu, Chengdong Faculty of Robot Science and Engineering Northeastern University Shenyang China

Underwater image restoration is pivotal for enhancing the visual perception of underwater robots by improving image visibility and quality. Typically, underwater images are compromised by color distortion, low contrast, and noise, which degrade the robots' environmental perception. This paper introduces FS-Net, an innovative approach that synergistically utilizes information from both the frequency and spatial domains to overcome these challenges. By strategically separating and processing different frequency components, FS-Net effectively reduces noise and preserves intricate details. Concurrently, advanced spatial domain filtering and enhancement techniques are employed to augment contrast and rectify color distortions. This dual-domain integration ensures a comprehensive and efficient restoration process. We rigorously evaluate FS-Net using a well-established underwater image dataset, where it demonstrates substantial enhancements in both visual quality and quantitative performance metrics over existing methods. This work significantly advances underwater imaging technology, offering a robust solution that markedly improves image clarity and utility for underwater robotic applications. © Published under licence by IOP Publishing Ltd.

关键词： Underwater photography

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Attention-Based Generative Grasping Deformable Convolutional Neural Network 9

Attention-Based Generative Grasping Deformable Convolutional...

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9th International Conference on robotics and Automation engineering, ICRAE 2024

作者： Qin, Kang-Xiong Fu, Ru-Jie Zhang, Sheng-Wei Jiang, Yang Northeastern University Faculty of Robot Science and Engineering Shenyang China

ISBN: (纸本)9798331518301

Traditional generative grasping convolutional neural network suffers from insufficient feature selection capability and limited adaptability to complex geometric transformations in unstructured environments. To address these issues, this study proposes an optimizational method based on the Convolutional Block Attention Module (CBAM) and Deformable Convolutional Networks (DCN) to improve the performance of Generative Grasping Convolutional Neural Network (GG-CNN) in robotic grasping tasks. The Network is trained and evaluated using the Cornell and Jacquard datasets, the simulation and real-world experiments are conducted. The experimental results show that the AGG-DCNN exhibits better performance in model training and grasping tasks, achieving an accuracy of 97.19% on the Jacquard dataset. Additionally, the network shows significant improvements in convergence speed and robustness in learning accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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SUGrasping: a semantic grasping framework based on multi-head 3D U-Net

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Multimedia Tools and Applications 2024年 1-22页

作者： Cao, He Zhang, Yunzhou Ge, Zhexue Chen, Xin Liu, Xiaozheng Zhao, Jiaqi Faculty of Robot Science and Engineering Northeastern University Shenyang China College of Information Science and Engineering Northeastern University Shenyang China College of Intelligence Science and Technology National University of Defense Technology Hunan Changsha410073 China

Object grasping is an important skill for robots to interact with the real world, especially in unstructured environments where occlusions and different shapes of target objects are present. In this work, we introduce a robot grasping pipeline called SUGrasping, which can obtain the grasping poses more precisely for target objects. The grasping pipeline treats the Truncated Signed Distance Function (TSDF) and point clouds of the grasping scene as input simultaneously. The proposed multi-head 3D U-Net accepts reconstructed TSDF representation and outputs the grasping configurations, including predicted grasp quality, orientation and width of the gripper. The point cloud is fed into PointNet to obtain the semantic segmentation results for all objects in the grasping workspace. With the help of point cloud inside the gripper, the relationship between the gripper and semantic information can be established. It makes robots know which object they are grasping, rather than just removing objects in the workspace like previous works. Experimental results show that the proposed method has an improvement in grasping success rate and percent cleared of target objects, which outperforms state-of-the-art methods compared in this paper. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantic Segmentation

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