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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Meng, Qiwei Zhu, Shiqiang Gu, Jianjun Lin, Zheyuan Zhong, Ling Song, Wei Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

In this paper, we present FEIF, a novel framework for 6D object pose estimation from a single RGBD image. Compared with previous approaches, this model fully leverages the complementary RGB and depth information through lay-erwise feature excitation and interactive fusion, thus improving the effectiveness and robustness of extracted features. The representative RGBD features can be subsequently applied for object keypoints prediction and pose computation. In addition, our FEIF includes a self-evaluation module to make robots aware of the confidence of pose estimation, so they are flexible to take the corresponding strategy for following tasks. Experiment results prove the SOTA performance of our model on LineMOD, Occlusion-LineMOD and YCB-Video datasets, and we also demonstrate its applicability and robustness in real-world robotic manipulation. © 2022 IEEE.

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HSMR: A Head-Shoulder Mask Aided ResNet to Guide Focus of Re-Identification Implemented on Tour-Guide Robot

HSMR: A Head-Shoulder Mask Aided ResNet to Guide Focus of Re...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Lin, Zheyuan Huang, Min Wang, Wen Qin, Mengjie Li, Te Wan, Minhong Gu, Jason Zhu, Shiqiang Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

For service robots, person re-identification (ReID) and multi-pedestrian tracking (MPT) are vital to get a person's location and link identities across frames. Though their accuracy keeps improving, most work lacks consideration of the application scenario, haunted by limited space, constrained power supply, and demands of the real-time response during human-robot interaction. Some ReID models learn trivial or unrelated features, inhibiting the downstream tasks. To solve these issues, the efficient and light-weighted Head-Shoulder Mask aided ResNet (HSMR) is proposed. This model applies multi-task learning to enhance the feature extraction performance in the training stage without extra computational load during inference. The auxiliary task fully uses head-shoulder information to guide the network and focuses on the head region, which contains the identity information. In experiments on the Tour-Guide Robot Data Base (TGRDB), HSMR earned results better than ResNet-18 on the ReID task and was superior to the recent two-stream method on the MPT task. On the mobile hardware, inference reaches an average of 15.2 FPS, three times faster than the two-stream method. The code is released at https://***/ZhYLin99/HSMR. © 2022 IEEE.

关键词： Human robot interaction

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A Multi-Agent Digital Twin Framework for AI-Driven Fitness Coaching 25

A Multi-Agent Digital Twin Framework for AI-Driven Fitness C...

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2025 ACM International Conference on Interactive Media Experiences, IMX 2025

作者： Vahdati, Monica Gholizadeh Hamlabadi, Kamran Laamarti, Fedwa El Saddik, Abdulmotaleb Multimedia Communications Research Laboratory (MCRLab) School of Electrical Engineering and Computer Science University of Ottawa Ottawa ON Canada Computer Vision Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) ON Abu Dhabi United Arab Emirates

ISBN: (纸本)9798400713910

We introduce DTAIFC, a modular Digital Twin AI Fitness Coaching system that delivers personalized feedback through multimodal interaction. The system combines OpenPose-based skeletal tracking with a Crew-inspired multi-agent architecture to analyze user posture and provide biomechanically grounded coaching in natural language and voice. At its core, an Orchestrator Agent coordinates Feedback and Recommendation Agents, leveraging short-term memory (Redis) for real-time session context and long-term memory (PostgreSQL) for user-specific historical insight. Language generation is powered by GPT-4, enabling adaptive, context-aware feedback through prompt-driven reasoning. DTAIFC operates asynchronously through a lightweight web interface, supporting input via static images, voice commands, and text queries. Unlike real-time systems that depend on continuous video or wearables, DTAIFC offers a scalable, privacy-conscious solution for intelligent fitness guidance in virtual environments. This framework establishes a new paradigm for memory-augmented, agentic AI coaching, advancing the integration of digital twins in human-centered applications. © 2025 Copyright held by the owner/author(s).

关键词： Agentic AI Digital Twins Fitness Coaching Large Language Model (LLM) Multi-Agent System

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Data-Efficient Control Barrier Function Refinement

arXiv

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arXiv 2023年

作者： Dai, Bolun Huang, Heming Krishnamurthy, Prashanth Khorrami, Farshad Control/Robotics Research Laboratory Electrical & Computer Engineering Department Tandon School of Engineering New York University BrooklynNY11201 United States

Control barrier functions (CBFs) have been widely used for synthesizing controllers in safety-critical applications. When used as a safety filter, it provides a simple and computationally efficient way to obtain safe controls from a possibly unsafe performance controller. Despite its conceptual simplicity, constructing a valid CBF is well known to be challenging, especially for high-relative degree systems under nonconvex constraints. Recently, work has been done to learn a valid CBF from data based on a handcrafted CBF (HCBF). Even though the HCBF gives a good initialization point, it still requires a large amount of data to train the CBF network. In this work, we propose a new method to learn more efficiently from the collected data through a novel prioritized data sampling strategy. A priority score is computed from the loss value of each data point. Then, a probability distribution based on the priority score of the data points is used to sample data and update the learned CBF. Using our proposed approach, we can learn a valid CBF that recovers a larger portion of the true safe set using a smaller amount of data. The effectiveness of our method is demonstrated in simulation on a unicycle and a two-link arm. © 2023, CC BY.

关键词： Controllers

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Low-cost Mars Terrain Classification System Based on Coarse-grained Annotation

Low-cost Mars Terrain Classification System Based on Coarse-...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Zhang, Jian Chen, Yeheng Zhu, Shiqiang Li, Yuehua Xie, Tian Gu, Jason Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

Due to communication latency with remote ground sites, automatic recognition of Mars terrain is essential for the path-planning of rovers. Currently, most vision-based terrain classification require thousands of fine-grained training samples, while the undefined terrains on Mars are difficult to be classified or fine-grained labeled. Actually, most of the terrain categories can only be coarse-grained labeled due to several limitations, such as overlapped sub-regions, blurred borders, etc. To solve this problem, CACMT (Coarse-grained Annotation-based Classification for Mars Terrain) is proposed to generate the global fine-grained classification map from the local coarse-grained data. Specifically, the complete pipeline is decomposed into (i) annotation rules with unique design, (ii) hierarchical feature fusion network for predicting sub-features of terrain (iii) and a generator for outputting dense terrain categories of Mars. Finally, the results of actual data on Mars demonstrate that the terrain sub-features can be successfully recognized and a dense terrain classification map can be generated applying only coarse-grained labeled images. © 2022 IEEE.

关键词： Motion planning

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OSAP‐Loss:Efficient optimization of average precision via involving samples after positive ones towards remote sensing image retrieval

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1191-1212页

作者： Xin Yuan Xin Xu Xiao Wang Kai Zhang Liang Liao Zheng Wang Chia‐Wen Lin School of Computer Science and Technology Wuhan University of Science and TechnologyWuhanChina Hubei Province Key Laboratory of Intelligent Information Processing and Real‐time Industrial System Wuhan University of Science and TechnologyWuhanChina Nanyang Technological University SingaporeSingapore School of Computer Science Wuhan UniversityWuhanChina Department of Electrical Engineering Industrial Technology Research InstituteNational Tsing Hua UniversityHsinchuChina

In existing remote sensing image retrieval(RSIR)datasets,the number of images among different classes varies dramatically,which leads to a severe class imbalance *** studies propose to train the model with the ranking‐based metric(e.g.,average precision[AP]),because AP is robust to class ***,current AP‐based methods overlook an important issue:only optimising samples ranking before each positive sample,which is limited by the definition of AP and is prone to local *** achieve global optimisation of AP,a novel method,namely Optimising Samples after positive ones&AP loss(OSAP‐Loss)is proposed in this ***,a novel superior ranking function is designed to make the AP loss differentiable while providing a tighter upper ***,a novel loss called Optimising Samples after Positive ones(OSP)loss is proposed to involve all positive and negative samples ranking after each positive one and to provide a more flexible optimisation strategy for each ***,a graphics processing unit memory‐free mechanism is developed to thoroughly address the non‐decomposability of AP *** experimental results on RSIR as well as conventional image retrieval datasets show the superiority and competitive performance of OSAP‐Loss compared to the state‐of‐the‐art.

关键词： computer vision image retrieval metric learning

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Differentiable Optimization Based Time-Varying Control Barrier Functions for Dynamic Obstacle Avoidance

arXiv

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arXiv 2023年

作者： Dai, Bolun Khorrambakht, Rooholla Krishnamurthy, Prashanth Khorrami, Farshad Control/Robotics Research Laboratory Electrical & Computer Engineering Department Tandon School of Engineering New York University BrooklynNY11201 United States

Control barrier functions (CBFs) provide a simple yet effective way for safe control synthesis. Recently, work has been done using differentiable optimization (diffOpt) based methods to systematically construct CBFs for static obstacle avoidance tasks between geometric shapes. In this work, we extend the application of diffOpt CBFs to perform dynamic obstacle avoidance tasks. We show that by using the time-varying CBF (TVCBF) formulation, we can perform obstacle avoidance for dynamic geometric obstacles. Additionally, we show how to extend the TVCBF constraint to consider measurement noise and actuation limits. To demonstrate the efficacy of our proposed approach, we first compare its performance with a model predictive control based method and a circular CBF based method on a simulated dynamic obstacle avoidance task. Then, we demonstrate the performance of our proposed approach in experimental studies using a 7-degree-of-freedom Franka research 3 robotic manipulator. © 2023, CC BY.

关键词： Model predictive control

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High Precision Analog Gauge Reader Using Optical Flow and computer vision

High Precision Analog Gauge Reader Using Optical Flow and Co...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Chavan, Shruti Yu, Xinrui Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States

ISBN: (纸本)9781665480093

This paper aims at developing algorithms to automate the process of reading analog gauges at different operational industries, healthcare sector and automobiles using Artificial Intelligence (AI) techniques. Proposed algorithms start with video stabilization using optical flow followed by image processing which performs segmentation using HSV color space and morphological operations. Next, Hough transform is applied to determine the dial region and pointer. Using the coordinates locating the pointer in the frame, angle made with minimum valued scale mark is calculated using trigonometry and evaluated reading is displayed in the form of text and a progress bar. The proposed algorithms for Analog Gauge Reader (AGR) is executed using open-source libraries such as OpenCV, NumPy, PIL with Python programming and it is developed in the PyCharm environment. The experimental results show significant decrease in the error with the introduction of image preprocessing and the generalizability of the algorithm to different types of analog gauges captured in different conditions of illuminance. The accuracy and precision of the algorithms is 99% to 100% in the experimental studies. © 2022 IEEE.

关键词： Hough transforms

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Soccer Player Recognition using Artificial Intelligence and computer vision

Soccer Player Recognition using Artificial Intelligence and ...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Diop, Charles-Alexandre Pelloux, Baptiste Yu, Xinrui Yi, Won-Jae Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9781665480093

Groundbreaking features and functionalities are available in sports broadcasting programs, specifically in soccer games, such as post-game analysis, tracking of players, tracking of the ball, and associated teams' and players' statistical information. However, viewers don't have control to choose which features to view on-demand and aren't able to interact with any of these functionalities. Our system aims for the fundamental system of an on-demand viewer-driven application that would be able to track players on the field, identify formation changes, follow team strategy changes, and gather all statistical information of the player and the team on a single screen. In order to realize such an application, we focus on developing a method to track players on the field using multiple Artificial Intelligence (AI) and computer vision techniques where our application employs facial recognition and jersey number recognition algorithms. As a live soccer game broadcast would have various camera views of the players, our custom-made database contains captured images from different scenarios of camera views such as different angles and zooms of the player and is used to train the model using Convolutional Neural Network (CNN). Our system is scalable to different types of sports, such as basketball, baseball, volleyball, and more where players have their numbers on their jerseys and would be feasible to apply our system since soccer involves more players on the field than other sports games. © 2022 IEEE.

关键词： Cameras

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Enhanced Detection of Demagnetization in Direct-Drive Wind Turbine Generators Using eMIST and Machine Learning

Enhanced Detection of Demagnetization in Direct-Drive Wind T...

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IEEE Workshop on electrical Machines Design, Control and Diagnosis (WEMDCD)

作者： Qiuyi Chen Panagiotis Tsilifis Jaime Renedo Anglada Department of Mechanical Engineering University of Maryland College Park MD USA Robotics AI & Computer Vision Group GE Vernova Advanced Research Niskayuna NY USA Electrical Systems GE Vernova Advanced Research Niskayuna NY USA

ISBN: (数字)9798331520748

ISBN: (纸本)9798331520755

The rapid growth of offshore wind energy needs robust condition monitoring to ensure the reliability and efficiency of direct-drive permanent magnet synchronous generators (PMSGs). Demagnetization, a critical fault in these generators, can severely impact performance and increase operational costs, especially in harsh marine environments. Traditional diagnostic techniques often struggle to detect subtle magnetic property changes under varying load conditions. This paper presents an innovative framework for demagnetization detection in PMSGs, leveraging the electrical Multi-phase Imbalance Separation Technique (eMIST) combined with machine learning models. Using a dataset from offshore wind turbines, including simulated faulty conditions, the proposed methodology enhances anomaly detection by isolating imbalance signatures and correlating them with key operational parameters. Gaussian Process Regression (GPR) is employed to predict anomalies, offering high accuracy and confidence in fault classification. Results demonstrate the approach's ability to identify early-stage demagnetization, enabling predictive maintenance, reducing downtime, and improving the economic viability of offshore wind farms.

关键词： Costs Accuracy Wind energy Machine learning Gaussian processes Wind farms Generators Wind turbines Demagnetization Anomaly detection

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