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JADFER: Exploring Spatial-Contextual Interaction with Joint Attention Dropping for Facial Expression Recognition

IEEE Transactions on Affective Computing 2024年第2期16卷 655-668页

作者： Gao, Yu Ren, Weihong Jiang, Weibo Dong, Qian Nie, Wei Wu, Wenhao Liu, Honghai Harbin Institute of Technology State Key Lab of Robotics and Systems School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China

Facial Expression Recognition (FER) aims to categorize emotional expressions depicted on a human face, and is a challenging task under unconstrained conditions, such as face occlusions and pose variations. Recent methods usually adopt self attention or cross attention to explore global or local relationships among different level features. However, these methods are inclined to focus on the redundant facial regions, causing model overfitting. To address this problem, we propose a new FER model named JADFER, which drops the joint attention in the weight matrix to adaptively enhance facial expression representations. Specifically, our JADFER model consists of three components: Spatial Branch (SB), Contextual Branch (CB), and Spatial-Contextual Interaction (SCI). Firstly, SB runs N paths in parallel, where a Variety loss is designed to guide the paths of SB to focus on different discriminative regions. Meanwhile, CB abstracts the contextual facial representations using self attention with Joint Attention Dropping (JAD). Then, the SCI adopts the spatial features from SB to query the contextual representations from CB through cross attention with JAD, which regulates the attention weights by dropping the similar activations to further enhance the facial embeddings. Experimental results demonstrate that the proposed model outperforms the state-of-the-art methods on several FER benchmarks. © 2010-2012 IEEE.

关键词： Face recognition

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A High-precision Calibration Method for Microsurgical Robots: Combining Iterative Least Squares and PSO-Optimized Support Vector Machine 11

A High-precision Calibration Method for Microsurgical Robots...

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11th International Conference on Mechatronics and robotics Engineering, ICMRE 2025

作者： Pang, Linjun Li, Haiming Gao, Peiyuan Li, Haotian Zhang, He Zhao, Jie Harbin Institute of Technology State Key Lab of Robotics and Systems Harbin China

ISBN: (纸本)9798331509293

Microsurgical robot calibration is essential for achieving high precision in surgical operations. This paper proposes a high-precision calibration approach combining Iterative Least Squares (ILS) for geometric error correction and Particle Swarm Optimization-optimized Least Squares Support Vector Machine (PSO-LSSVM) for non-geometric error compensation. Experimental results show that the proposed method reduces positioning errors from 3.2541 mm to 0.2310 mm through geometric calibration, achieving a 92.90 % accuracy improvement, and further reduces the error to 0.0749 mm after non-geometric error compensation. These results underscore the effectiveness of the proposed approach in enhancing the precision of robotic systems used in microsurgery, © 2025 IEEE.

关键词： Error compensation

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Relay Selection and Power Control for Mobile Underwater Acoustic Communication Networks: A Dual-Thread Reinforcement Learning Approach

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年第2期9卷 1-1页

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Key Lab of Industrial Computer Control Engineering of Hebei Province China Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China

This paper deals with a cooperation communication problem (relay selection and power control) for mobile underwater acoustic communication networks. To achieve satisfactory transmission capacity, we propose a reinforcement-learning-based cooperation communication scheme to efficiently resist the highly dynamic communication links and strongly unknown time-varying channel states caused by the mobility of Autonomous Underwater Vehicles (AUVs). Firstly, a particular Markov decision process is developed to model the dynamic relay selection process of mobile AUV in the unknown scenario. In the developed model, an experimental statistical-based partition mechanism is proposed to cope with the greatly increasing dimension of the state space caused by the mobility of AUV, reducing the search optimization difficulty. Secondly, a dual-thread reinforcement learning structure with actual and virtual learning threads is proposed to efficiently track the superior relay action. In the actual learning thread, the proposed improved probability greedy policy enables the AUV to strengthen the exploration for the reward information of potential superior relays on the current state. Meanwhile, in the virtual learning thread, the proposed upper-confidence-bound-index-based uncertainty estimation method can estimate the action-reward level of historical states. Consequently, the combination of actual and virtual learning threads can efficiently obtain satisfactory Q value information, thereby making superior relay decision-making in a short time. Thirdly, a power control mechanism is proposed to reuse the current observed action-reward information and transform the multiple unknown parameter nonlinear joint power optimization problem into a convex optimization problem, thereby enhancing network transmission capacity. Finally, simulation results verify the effectiveness of the proposed scheme. IEEE

关键词： Convex optimization

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Recent Developments in Metallic Degradable Micromotors for Biomedical and Environmental Remediation Applications

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Nano-Micro Letters 2024年第3期16卷 1-35页

作者： Sourav Dutta Seungmin Noh Roger Sanchis Gual Xiangzhong Chen Salvador Pané Bradley J.Nelson Hongsoo Choi Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science and Technology(DGIST)Daegu 42988Republic of Korea DGIST‑ETH Microrobotics Research Center DGISTDaegu 42988Republic of Korea Multi‑Scale Robotics Lab Institute of Robotics and Intelligent SystemsETH Zurich8092 ZurichSwitzerland Institute of Optoelectronics State Key Laboratory of Photovoltaic Science and TechnologyShanghai Frontiers Science Research Base of Intelligent Optoelectronics and PerceptionFudan UniversityShanghai 200433People’s Republic of China

Synthetic micromotor has gained substantial attention in biomedicine and environmental ***-based degradable micromotor composed of magnesium(Mg),zinc(Zn),and iron(Fe)have promise due to their nontoxic fuel-free propulsion,favorable biocompatibility,and safe excretion of degradation products Recent advances in degradable metallic micromotor have shown their fast movement in complex biological media,efficient cargo delivery and favorable biocompatibility.A noteworthy number of degradable metal-based micromotors employ bubble propulsion,utilizing water as fuel to generate hydrogen *** novel feature has projected degradable metallic micromotors for active in vivo drug delivery *** addition,understanding the degradation mechanism of these micromotors is also a key parameter for their design and *** propulsion efficiency and life span govern the overall performance of a degradable metallic *** we review the design and recent advancements of metallic degradable ***,we describe the controlled degradation,efficient in vivo drug delivery,and built-in acid neutralization capabilities of degradable micromotors with versatile biomedical ***,we discuss micromotors’efficacy in detecting and destroying environmental ***,we address the limitations and future research directions of degradable metallic micromotors.

关键词： Magnesium Zinc Iron Biodegradable microrobot Biomedical Environmental

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A High-precision Calibration Method for Microsurgical Robots: Combining Iterative Least Squares and PSO-Optimized Support Vector Machine

A High-precision Calibration Method for Microsurgical Robots...

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Mechatronics and robotics Engineering (ICMRE), International Conference on

作者： Linjun Pang Haiming Li Peiyuan Gao Haotian Li He Zhang Jie Zhao State Key Lab of Robotics and Systems Harbin Institute of Technology Harbin China

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

关键词： Support vector machines Accuracy Medical robotics Error compensation Kinematics Microsurgery Calibration Iterative methods Reliability Particle swarm optimization

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MLPER: Multi-Level Prompts for Adaptively Enhancing Vision-Language Emotion Recognition

MLPER: Multi-Level Prompts for Adaptively Enhancing Vision-L...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Yu Gao Weihong Ren Xinglong Xu Yan Wang Zhiyong Wang Honghai Liu School of Mechanical Engineering and Automation State Key Lab of Robotics and Systems Harbin Institute of Technology Shenzhen China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In the field of robotics, vision-based Emotion Recognition (ER) has achieved significant progress, but it still faces the challenge of poor generalization ability under unconstrained conditions (e.g., occlusions and pose variations). In this work, we propose MLPER model, which introduces Vision-Language Model for Emotion Recognition to learn discriminative representations adaptively. Specifically, different from typically leveraging a hand-crafted prompt (e.g., "a photo of a [class] person"), we first establish Multi-Level Prompts from three aspects: facial expression, human posture and situational condition using large language models, like ChatGPT. Correspondingly, we extract the visual tokens from three levels: the face, body, and context. Further, to achieve fine-grained alignment at each level, we adopt textual tokens from the positive and the hard negative to query visual tokens, predicting whether a pair of image and text is matched. Experimental results demonstrate that our MLPER model outperforms the state-of-the-art methods on several ER benchmarks, especially under the conditions of occlusions and pose variations.

关键词： Emotion recognition Adaptation models Visualization Large language models Human-robot interaction Medical services Benchmark testing Chatbots Faces Intelligent robots

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Spontaneous Emergence of Multitasking in Minimal Robotic systems 1

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16th International Conference on Intelligent robotics and Applications, ICIRA 2023

作者： Zhang, Ji Li, Han Du, Haoyuan Liang, Yiming Song, Wei Li, Tiefeng Zhejiang Lab Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou310027 China

ISBN: (数字)9789819964987

ISBN: (纸本)9789819964970

This study investigates the behavior of a system of identical robots modeled as self-propelled particles using the Vicsek model. The focus is on understanding the emergence of a chimera state, which represents the spontaneous emergence of multitasking capabilities in the robot swarm. The study considers scenarios with limited communication in a minimal system of 3 identical robots. Numerical simulations and stability analysis are conducted to analyze the system’s characteristics and motion patterns. The research aims to provide insights into the underlying mechanisms and develop a comprehensive framework for controlling and understanding the emergence of the chimera state in robot swarm systems. Additionally, the study examines the influence of coupling strength and phase lag on the occurrence of chimera states through direct numerical simulations and theoretical analysis. The results reveal the relationship between system structure and the manifestation of chimera states, providing valuable insights for further research in this field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Robots

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Design of a Needle Guidance Based a Variable Stiffness Continuum Manipulator for Brain Surgery

Design of a Needle Guidance Based a Variable Stiffness Conti...

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IEEE International Conference on Mechatronics and Automation

作者： Mohamed A. Naeem Wenpeng Gao Yili Fu State Key Lab of Robotics and Systems Harbin Institute of Technology Harbin China School of Life Science and Technology Harbin Institute of Technology Harbin China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Owing to their continuously bending backbone, continuum robots are able to perform dexterous movements and accurate manipulation, allowing them suited for unstructured environments such as surgical applications. However, controlling their stiffness and payload capability are still challenging. This paper presents a novel design of continuum robot for needle guidance in brain surgery. The design composes of a series-connected rigid spherical joints, and a central cable acting as a backbone to adjust stiffness of the robot. Additionally, this cable offers a mechanical lock to precisely reach the target pose for the needle during Intracerebral Hemorrhage (ICH) evacuation surgery. A design studies are conducted by optimizing certain parameters to evaluate the performance of the robot in terms of variable stiffness and locking. The design analysis is performed, while respecting the constraints of stress and deflection. On the other hand, a friction analysis based on contact point estimation approach is derived to ensure the configuration lock. The performance of the proposed design is evaluated by using Multibody Dynamics software. Moreover, experimental validation of the robot is carried out to verify its variable stiffness and payload capacity. The results demonstrate that the continuum robot has an acceptable load-carrying capacity and displacement deflection error. The displacement deflection error range from 0.78 mm to 0.48 mm at payload of 500 gm and tension force range from 350 N to 800 N.

关键词： Force Aluminum Surgery Bending Needles Continuum robots Wire

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Anti-Degeneracy Scheme for Lidar SLAM based on Particle Filter in Geometry Feature-Less Environments

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IEEE robotics and Automation Letters 2025年第7期10卷 6784-6791页

作者： Li, Yanbin Zhang, Wei Zhang, Zhiguo Shi, Xiaogang Li, Ziruo Zhang, Mingming Xie, Hongping Chi, Wenzheng Beijing University of Posts and Telecommunications School of Electronics Engineering Beijing100876 China China Agricultural University Key Lab of Smart Agriculture Systems Beijing100083 China Beihang University School of Integrated Circuit Science and Engineering Beijing100191 China State Grid Jiangsu Electric Power Co.Ltd Construction Branch Jiangsu226000 China Soochow University Robotics and Microsystems Center School of Mechanical and Electric Engineering Suzhou215021 China

Simultaneous localization and mapping (SLAM) based on particle filtering has been extensively employed in indoor scenarios due to its high efficiency. However, in geometry feature-less scenes, the accuracy is severely reduced due to lack of constraints. In this article, we propose an anti-degeneracy system based on deep learning. Firstly, we design a scale-invariant linear mapping to convert coordinates in continuous space into discrete indexes, in which a data augmentation method based on Gaussian model is proposed to ensure the model performance by effectively mitigating the impact of changes in the number of particles on the feature distribution. Secondly, we develop a degeneracy detection model (DD-Model) using residual neural networks (ResNet) and transformer which is able to identify degeneracy by scrutinizing the distribution of the particle population. Thirdly, an adaptive anti-degeneracy strategy is designed, which first performs fusion and perturbation on the resample process to provide rich and accurate initial values for the pose optimization, and use a hierarchical pose optimization combining coarse and fine matching, which is able to adaptively adjust the optimization frequency and the sensor trustworthiness according to the degree of degeneracy, in order to enhance the ability of searching the global optimal pose. Finally, we demonstrate the optimality of model, as well as the improvement of image matrix method and GPU on the inference time through ablation experiments. The effectiveness of our method is verified by comparing with SOTA methods. © 2016 IEEE.

关键词： Deep learning

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GroupTrack: Multi-Object Tracking by Using Group Motion Patterns

GroupTrack: Multi-Object Tracking by Using Group Motion Patt...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Xinglong Xu Weihong Ren Gan Sun Haoyu Ji Yu Gao Honghai Liu State Key Lab of Robotics and Systems School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China School of Automation Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

The main challenge of Multi-Object Tracking (MOT) lies in maintaining a distinctive identity for each target in dense crowds or occluded scenarios. Although the existing methods have achieved significantly progress by using robust object detectors or complex association strategies, they cannot effectively solve long-term tracking due to individually motion or appearance modeling for each single target. In this paper, we propose a novel 2D MOT tracker GroupTrack, to learn reliable motion state for each target using group motion patterns. Specifically, for each tracklet, we first choose its neighboring ones to form a group of motion patterns, which can provide informative clues for the motion estimation of the current tracklet. Then, we apply the group motion patterns to perform tracklet prediction and data association. By integrating prior from neighboring motion patterns into the data association process, GroupTrack provides a new paradigm for target motion modeling in extremely crowded and occluded scenarios. Through extensive experiments on the public MOT17 and MOT20 datasets, we demonstrate the effectiveness of our approach in challenging scenarios and show state-of-the-art performance at various MOT metrics.

关键词： Measurement Target tracking Three-dimensional displays Tracking Motion estimation Detectors Reliability Object tracking Pattern matching Intelligent robots

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