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IEEE International Conference on robotics and Automation (ICRA)

作者： Honghui Wang Weiming Zhi Gustavo Batista Rohitash Chandra Transitional Artificial Intelligence Research Group School of Mathematics and Statistics UNSW Sydney Sydney Australia Robotics Institute Carnegie Mellon University Pittsburgh USA School of Computer Science and Engineering UNSW Sydney Sydney Australia

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Pedestrian trajectory prediction plays an important role in autonomous driving systems and robotics. Recent work utilizing prominent deep learning models for pedestrian motion prediction makes limited a priori assumptions about human movements, resulting in a lack of explainability and explicit constraints enforced on predicted trajectories. We present a dynamics-based deep learning framework with a novel asymptotically stable dynamical system integrated into a Transformer-based model. We use an asymptotically stable dynamical system to model human goal-targeted motion by enforcing the human walking trajectory, which converges to a predicted goal position, and to provide the Transformer model with prior knowledge and explainability. Our framework features the Transformer model that works with a goal estimator and dynamical system to learn features from pedestrian motion history. The results show that our framework outperforms prominent models using five benchmark human motion datasets.

关键词： Deep learning Legged locomotion Pedestrians Predictive models Benchmark testing Transformers Trajectory

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Human-centered AI and robotics

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AI Perspectives 2022年第1期4卷 1-14页

作者： Doncieux, Stephane Chatila, Raja Straube, Sirko Kirchner, Frank Institute of Intelligent Systems and Robotics (ISIR) Sorbonne Université CNRS Paris France Robotics Innovation Center DFKI GmbH (German Research Center for Artificial Intelligence) Bremen Germany Faculty of Mathematics and Computer Science Robotics Group University of Bremen Bremen Germany

robotics has a special place in AI as robots are connected to the real world and robots increasingly appear in humans everyday environment, from home to industry. Apart from cases were robots are expected to completely replace them, humans will largely benefit from real interactions with such robots. This is not only true for complex interaction scenarios like robots serving as guides, companions or members in a team, but also for more predefined functions like autonomous transport of people or goods. More and more, robots need suitable interfaces to interact with humans in a way that humans feel comfortable and that takes into account the need for a certain transparency about actions taken. The paper describes the requirements and state-of-the-art for a human-centered robotics research and development, including verbal and non-verbal interaction, understanding and learning from each other, as well as ethical questions that have to be dealt with if robots will be included in our everyday environment, influencing human life and societies.

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Entropy-Guided Reinforced Open World Active 3D Object Detection Learning

Entropy-Guided Reinforced Open World Active 3D Object Detect...

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Chinese Automation Congress (CAC)

作者： Haozhe Zhang Liyan Ma Shihui Ying Institute of Artificial Intelligence Shanghai University Shanghai China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Computer Engineering and Science School of Mechatronic Engineering and Automation Shanghai University Shanghai China Department of Mathematics School of Science Shanghai University Shanghai China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Traditional fully annotated closed set 3D object detection methods improve model performance but are impractical in real-world settings due to the emergence of new categories and the complexity of 3D annotations. Open-World Object Detection (OWOD) addresses these issues but relies heavily on manual labeling, which is costly. This paper focuses on open world active learning and proposes an entropy-guided reinforced open world active 3D object detection (EROA). EROA regards active learning as a reinforcement learning problem tailored for open driving scenarios. We use entropy as a reward metric for efficient reinforcement learning. We also leverage knowledge from the 2D domain using object-level large-scale vision-language models to enhance sample selection. Extensive experiments evidence that the proposed EROA meets the dynamic and cost-sensitive requirements of autonomous driving, enabling real-time detection of both known and unknown objects.

关键词： Measurement Solid modeling Three-dimensional displays Active learning Object detection Reinforcement learning Entropy Real-time systems Labeling Autonomous vehicles

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Clustering With Adaptive Graph Learning and Spectral Rotation 7

Clustering With Adaptive Graph Learning and Spectral Rotatio...

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7th International Conference on Vision, Image and Signal Processing, ICVISP 2023

作者： Yuan, Ge Zhou, Jie Huang, Chucheng Gao, Can Wang, Yangbo Shen, Xinrui National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China College of Mathematics and Statistics Shenzhen University Guangdong Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China

ISBN: (纸本)9781839539848

Prototype-based clustering algorithms have garnered considerable attention in the field of machine learning due to their efficiency and interpretability. Nonetheless, these algorithms often face performance degradation when confronted with high-dimensional or non-ellipsoidal data distributions. To surmount these challenges, this study introduces a novel clustering approach, dubbed Clustering with Adaptive Graph learning and Spectral Rotation (CAGSR). In CAGSR, the imposed spectral rotation operation mitigates the discrepancy between the membership matrix, which adheres to the notion of fuzzy clustering, and the spectral representations derived from an adaptive graph rather than a predefined one. This enables the generation of a comprehensive representation of the data across multiple spaces. Furthermore, the clustering and graph learning tasks are jointly optimized in a projected subspace, which can effectively reduce the adverse impact caused by irrelevant features in the original space. The proposed method seamlessly integrates fuzzy clustering, graph structure learning, and spectral rotation into a unified model, facilitating the detection of intrinsic structures. Experimental evaluations conducted on benchmark data sets substantiate the effectiveness of CAGSR when compared to related clustering approaches. © The Institution of Engineering & Technology 2023.

关键词： Fuzzy clustering

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Research on Auxiliary Diagnosis Technology of Chronic Disease Based on Case-Based Reasoning

Research on Auxiliary Diagnosis Technology of Chronic Diseas...

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2021 IEEE International Conference on Advances in Electrical Engineering and computer Applications, AEECA 2021

作者： Zhang, Lin Qi, Ping Zhang, Hao Institute of Robotics Engineering Anhui Sanlian University Anhui Hefei China Institute of Service Computing Tongling University Anhui Tongling China College of Mathematics and Computer Science Tongling University Anhui Tongling China

ISBN: (纸本)9781665435611

In recent years, with the continuous acceleration of social progress, the number of patients with chronic diseases worldwide has increased year by year, and the pressure on medical institutions and volunteer workers has also increased. Especially in the current situation of rampant CoviD-19, if the number of admissions and treatments for chronic patients can be reduced, it will help reduce the pressure on hospitals and medical staff during the special period at this stage, and will also reduce the spread and infection rate of CoviD-19. The research team established big health data through questionnaire surveys, and established a case database based on these data, and proposed an assisted diagnosis technology for chronic diseases based on case-based reasoning. With the continuous expansion of the application field of Case-Based Reasoning (CBR) technology, the problem of programmers' difficulty in obtaining and expressing professional knowledge has become increasingly prominent. Therefore, this article first gives the standardized expression of health data, and combines the method based on case-based reasoning with the scientific measurement of keyword weight (Term Frequency-Inverse Document Frequency, TF-IDF) to give the weight in the case retrieval process It also designs the calculation of the dispersion degree between classes for the distribution of characteristic words, which provides an effective method for the establishment of an auxiliary diagnosis model for chronic diseases. © 2021 IEEE.

关键词： Case based reasoning

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Efficient Tactile Sensing-based Learning from Limited Real-world Demonstrations for Dual-arm Fine Pinch-Grasp Skills

Efficient Tactile Sensing-based Learning from Limited Real-w...

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

作者： Xiaofeng Mao Yucheng Xu Ruoshi Wen Mohammadreza Kasaei Wanming Yu Efi Psomopoulou Nathan F. Lepora Zhibin Li School of Informatics University of Edinburgh UK Touchlab Limited Edinburgh U.K. Oxford Robotics Institute University of Oxford UK Department of Engineering Mathematics University of Bristol UK Department of Computer Science University College London UK

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Imitation learning for robot dexterous manipulation, especially with a real robot setup, typically requires a large number of demonstrations. In this paper, we present a data-efficient learning from demonstration framework which exploits the use of rich tactile sensing data and achieves fine bimanual pinch grasping. Specifically, we employ a convolutional autoencoder network that can effectively extract and encode high-dimensional tactile information. Further, we develop a framework that achieves efficient multi-sensor fusion for imitation learning, allowing the robot to learn contact-aware sensorimotor skills from demonstrations. The ablation studies on encoded tactile features highlighted the effectiveness of incorporating rich contact information, which enabled dexterous bimanual grasping with active contact searching. Extensive experiments demonstrated the robustness of the fine pinch grasp policy directly learned from few-shot demonstration, including grasping of the same object with different initial poses, generalizing to ten unseen new objects, robust and firm grasping against external pushes, as well as contact-aware and reactive re-grasping in case of dropping objects under very large perturbations. Furthermore, the saliency map analysis method is used to describe weight distribution across various modalities during pinch grasping, confirming the effectiveness of our framework at leveraging multimodal information. The video is available online at: https://***/BlzxGgiKfck.

关键词： Imitation learning Perturbation methods Autoencoders Grasping Robot sensing systems Feature extraction Robustness Sensors Data mining Intelligent robots

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Towards Continuous Time Finite Horizon LQR Control in SE(3)

arXiv

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arXiv 2023年

作者： Kumar, Shivesh Mueller, Andreas Wensing, Patrick Kirchner, Frank Bremen Germany Institute of Robotics Johannes Kepler University Linz Austria Department of Aerospace and Mechanical Engineering University of Notre Dame United States Faculty of Mathematics and Computer Science University of Bremen Germany

The control of free-floating robots requires dealing with several challenges. The motion of such robots evolves on a continuous manifold described by the Special Euclidean Group of dimension 3, known as SE(3). Methods from finite horizon Linear Quadratic Regulators (LQR) control have gained recent traction in the robotics community. However, such approaches are inherently solving an unconstrained optimization problem and hence are unable to respect the manifold constraints imposed by the group structure of SE(3). This may lead to small errors, singularity problems and double cover issues depending on the choice of coordinates to model the floating base motion. In this paper, we propose the use of canonical exponential coordinates of SE(3) and the associated Exponential map along with its differentials to embed this structure in the theory of finite horizon LQR controllers. © 2023, CC BY.

关键词： Linear control systems

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ON ACCELERATING EDGE AI: OPTIMIZING RESOURCE-CONSTRAINED ENVIRONMENTS

arXiv

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arXiv 2025年

作者： Sander, Jacob Cohen, Achraf Dasari, Venkat R. Venable, Brent Jalaian, Brian Department of Computer Science University of West Florida PensacolaFL United States Department of Mathematics and Statistics University of West Florida PensacolaFL United States DEVCOM Army Research Laboratory Aberdeen Proving Ground MD United States Department of Intelligent Systems and Robotics University of West Florida Institute for Human & Machine Cognition PensacolaFL United States Department of Computer Science Department of Intelligent Systems and Robotics University of West Florida Institute for Human & Machine Cognition PensacolaFL United States

Resource-constrained edge deployments demand AI solutions that balance high performance with stringent compute, memory, and energy limitations. In this survey, we present a comprehensive overview of the primary strategies for accelerating deep learning models under such constraints. First, we examine model compression techniques-pruning, quantization, tensor decomposition, and knowledge distillation-that streamline large models into smaller, faster, and more efficient variants. Next, we explore Neural Architecture Search (NAS), a class of automated methods that discover architectures inherently optimized for particular tasks and hardware budgets. We then discuss compiler and deployment frameworks, such as TVM, TensorRT, and OpenVINO, which provide hardware-tailored optimizations at inference time. By integrating these three pillars into unified pipelines, practitioners can achieve multi-objective goals, including latency reduction, memory savings, and energy efficiency-all while maintaining competitive accuracy. We also highlight emerging frontiers in hierarchical NAS, neurosymbolic approaches, and advanced distillation tailored to large language models, underscoring open challenges like pre-training pruning for massive networks. Our survey offers practical insights, identifies current research gaps, and outlines promising directions for building scalable, platform-independent frameworks to accelerate deep learning models at the edge. © 2025, CC BY.

关键词： Budget control

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Comparative Analysis of Two Simulation Environments for Robots, Gazebo, and CoppeliaSim in the Context of Their Use for Teaching Students a Course in Robotic Systems Modeling

Comparative Analysis of Two Simulation Environments for Robo...

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2024 International Conference "Quality Management, Transport and Information Security, Information Technologies", QM and TIS and IT 2024

作者： Tseeva, Fatimat M. Shogenova, Mariyana M. Senov, Khamisha M. Liana, Kanukoeva V. Bozieva, Asiyat M. Institute of Computer Science Electronics and Robotics Federal State Budgetary Educational Institution of Higher Education Kabardino-Balkarian State University Named after H.M. Berbekov Nalchik Russia Institute of Architecture Construction and Design Federal State Budgetary Educational Institution of Higher Education Kabardino-Balkarian State University Named after H.M. Berbekov Nalchik Russia Institute of Phisics and Mathematics Federal State Budgetary Educational Institution of Higher Education Kabardino-Balkarian State University Named after H.M. Berbekov Nalchik Russia

ISBN: (纸本)9798350367522

This article presents a comparative analysis of two simulation environments for robots and robotic systems - Gazebo and CoppeliaSim. The study evaluates their functionality and effectiveness for teaching students robot and robotic system modeling. The analysis was conducted based on the following criteria: user interface convenience, availability of educational materials, simulation realism, support for various types of robots and sensors, and integration capabilities with other software. As a result, our research allowed us to identify the main advantages and disadvantages of each simulation environment. These findings will help determine the most suitable tool for robotic education, thereby improving the quality of training for professionals in this field. © 2024 IEEE.

关键词： Students

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Deep learning for brain age estimation: A systematic review

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Information Fusion 2023年 96卷 130-143页

作者： Tanveer, M. Ganaie, M.A. Beheshti, Iman Goel, Tripti Ahmad, Nehal Lai, Kuan-Ting Huang, Kaizhu Zhang, Yu-Dong Del Ser, Javier Lin, Chin-Teng Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Robotics University of Michigan Ann ArborMI48109 United States Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Biomedical Imaging Lab National Institute of Technology Silchar Assam 788010 India Department of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Data Science Research Center Duke Kunshan University China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom Derio48160 Spain Bilbao48013 Spain School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated with the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required to elicit accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models. © 2023 Elsevier B.V.

关键词： Deep neural networks

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