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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Chuxi Fang Shiliang Shao Ting Wang Xianzhe Zhao Fengkui Cao Hai Zhao School of Computer Science And Engineering Northeastern University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

As an expanding field that caters to the needs of the elderly and people with disabilities, intelligent wheelchairs aim to enhance the efficiency and safety of individuals with disabilities. Path planning, a pivotal technology under intelligent wheelchair systems, has emerged as a focal point of scholarly exploration. This paper proposes advancements in the vector field histogram plus (VFH+) algorithm, thereby introducing the adaptive VFH+ algorithm (AVFH+). Notably, this paper redefines the calculation of obstacle density and introduces the concept of custom target point density. By dynamically modulating the threshold, we effectively resolve the problems of oscillatory paths during obstacle avoidance, thus averting the predicament of the robot. In addition, the robot's speed is adaptively adjusted based on the opening width, and the trajectory is smoothed using Bezier path planning, enabling the robot to reach the target position through a shorter collision-free and smooth path. The improved AVFH+ algorithm demonstrates excellent path planning capability through multiple simulation experiments in various map environments with different starting points and target locations. The algorithm has the ability to adapt to complex environments, accurately perceive and avoid obstacles, and exhibits significant generalization capabilities. This makes it significant in dense area path planning, providing safer and more efficient mobility solutions in areas such as smart wheelchairs, and actively contributing to the quality of life of people with disabilities. Future research could explore the consideration of dynamic obstacles and validate the performance of the algorithm in real-world environments.

关键词： Histograms Heuristic algorithms Wheelchairs People with disabilities Vectors Trajectory Safety

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SemVecNet: Generalizable Vector Map Generation for Arbitrary Sensor Configurations

SemVecNet: Generalizable Vector Map Generation for Arbitrary...

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IEEE Symposium on Intelligent Vehicle

作者： Narayanan Elavathur Ranganatha Hengyuan Zhang Shashank Venkatramani Jing-Yan Liao Henrik I. Christensen Students of Contextual Robotics Institute University of California San Diego CA USA Faculty of the Department of Computer Science and Engineering University of California San Diego CA USA

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Vector maps are essential in autonomous driving for tasks like localization and planning, yet their creation and maintenance are notably costly. While recent advances in online vector map generation for autonomous vehicles are promising, current models lack adaptability to different sensor configurations. They tend to overfit to specific sensor poses, leading to decreased performance and higher retraining costs. This limitation hampers their practical use in real-world applications. In response to this challenge, we propose a modular pipeline for vector map generation with improved generalization to sensor configurations. The pipeline leverages probabilistic semantic mapping to generate a bird’s-eye-view (BEV) semantic map as an intermediate representation. This intermediate representation is then converted to a vector map using the MapTRv2 decoder. By adopting a BEV semantic map robust to different sensor configurations, our proposed approach significantly improves the generalization performance. We evaluate the model on datasets with sensor configurations not used during training. Our evaluation sets includes larger public datasets, and smaller scale private data collected on our platform. Our model generalizes significantly better than the state-of-the-art methods. The code will be available at https://***/AutonomousVehicleLaboratory/SemVecNet.

关键词： Training Semantic segmentation Pipelines Semantics Probabilistic logic Vectors Planning

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Distributed Safe Coverage Control of Multi-agent Systems with Energy Constraints

Distributed Safe Coverage Control of Multi-agent Systems wit...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Chengkai Qu Chao Zhai Hehong Zhang Yuanlong Xie School of Automation China University of Geosciences Wuhan China College of Computer and Data Science Fuzhou University Fuzhou China Guangdong Intelligent Robotics Institute Guangdong China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

In this article, we propose a continuous monitoring method to enhance disaster warning capabilities and address the persistent coverage problem in multi-agent systems. To enable the agent to continuously perform tasks, we transform the optimization objective function under energy constraints into constraints on control inputs. Additionally, the energy of the agent is constrained through the control barrier function (CBF), and a corresponding control framework is designed to achieve persistent task execution. We apply the proposed method to landslide early warning and validate its effectiveness and superiority in persistent coverage through numerical simulation.

关键词： Disasters Transforms Numerical simulation Linear programming Control systems Terrain factors Task analysis

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Multi-robot Feature-based SLAM using Submap Joining

Multi-robot Feature-based SLAM using Submap Joining

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2021 Australasian Conference on robotics and Automation, ACRA 2021

作者： Chen, Shengduo Han, Ruihua Zhao, Liang Hang, Shoudong Hao, Qi Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Robotics Institute of University of Technology Sydney Australia The Department of Computer Science The University of Hong Kong Hong Kong

ISBN: (纸本)9781713841487

This paper considers the feature-based SLAM using multiple robots. To reduce the computational complexity and data storage, a distributed multi-robot feature-based SLAM algorithm under submap joining scheme is proposed. Each robot first independently builds a submap using the information collected by its sensors. Once the robots can observe each other, the submaps can then be fused together to obtain a global map. We implemented and tested the proposed algorithm in both simulation and real world environments. Both simulation and experimental results have validated the robustness and accuracy of the proposed algorithm. © 2021 Australasian robotics and Automation Association. All rights reserved.

关键词： Digital storage

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Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series Classification

arXiv

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arXiv 2025年

作者： Yang, Wennuo Wu, Shiling Zhou, Yuzhi Luo, Cheng He, Xilin Xie, Weicheng Shen, Linlin Song, Siyang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Provincial Key Laboratory of Intelligent Information Processing China HBUG Lab University of Exeter United Kingdom

Multivariate Time Series Classification (MTSC) enables the analysis if complex temporal data, and thus serves as a cornerstone in various real-world applications, ranging from healthcare to finance. Since the relationship among variables in MTS usually contain crucial cues, a large number of graph-based MTSC approaches have been proposed, as the graph topology and edges can explicitly represent relationships among variables (channels), where not only various MTS graph representation learning strategies but also different Graph Neural Networks (GNNs) have been explored. Despite such progresses, there is no comprehensive study that fairly benchmarks and investigates the performances of existing widely-used graph representation learning strategies/GNN classifiers in the application of different MTSC tasks. In this paper, we present the first benchmark which systematically investigates the effectiveness of the widely-used three node feature definition strategies, four edge feature learning strategies and five GNN architecture, resulting in 60 different variants for graph-based MTSC. These variants are developed and evaluated with a standardized data pipeline and training/validation/testing strategy on 26 widely-used suspensor MTSC datasets. Our experiments highlight that node features significantly influence MTSC performance, while the visualization of edge features illustrates why adaptive edge learning outperforms other edge feature learning methods. The code of the proposed benchmark is publicly available at https://***/CVI-yangwn/*** Codes 68T10 © 2025, CC BY.

关键词： Graph neural networks

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GraspGPT: Leveraging Semantic Knowledge from a Large Language Model for Task-Oriented Grasping

arXiv

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arXiv 2023年

作者： Tang, Chao Huang, Dehao Ge, Wenqi Liu, Weiyu Zhang, Hong Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta United States

Task-oriented grasping (TOG) refers to the problem of predicting grasps on an object that enable subsequent manipulation tasks. To model the complex relationships between objects, tasks, and grasps, existing methods incorporate semantic knowledge as priors into TOG pipelines. However, the existing semantic knowledge is typically constructed based on closed-world concept sets, restraining the generalization to novel concepts out of the pre-defined sets. To address this issue, we propose GraspGPT, a large language model (LLM) based TOG framework that leverages the open-end semantic knowledge from an LLM to achieve zero-shot generalization to novel concepts. We conduct experiments on Language Augmented TaskGrasp (LA-TaskGrasp) dataset and demonstrate that GraspGPT outperforms existing TOG methods on different held-out settings when generalizing to novel concepts out of the training set. The effectiveness of GraspGPT is further validated in real-robot experiments. Our code, data, appendix, and video are publicly available at https://***/view/graspgpt. Copyright © 2023, The Authors. All rights reserved.

关键词： Zero-shot learning

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Unified regularity measures for sample-wise learning and generalization

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Visual Intelligence 2024年第1期2卷 1-20页

作者： Zhang, Chi Yuan, Meng Ma, Xiaoning Liu, Yu Lu, Haoang Wang, Le Su, Yuanqi Liu, Yuehu Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an 710049 China National KeyLaboratory of Human-Machine Hybrid Augmented Intelligence Xi’an Jiaotong University Xi’an 710049 China National Engineering Research Center for Visual Information and Applications Xi’an Jiaotong University Xi’an 710049 China School of Computer Science and Technology Xi’an Jiaotong University Xi’an 710049 China

Fundamental machine learning theory shows that different samples contribute unequally to both the learning and testing processes. Recent studies on deep neural networks (DNNs) suggest that such sample differences are rooted in the distribution of intrinsic pattern information, namely sample regularity. Motivated by recent discoveries in network memorization and generalization, we propose a pair of sample regularity measures with a formulation-consistent representation for both processes. Specifically, the cumulative binary training/generalizing loss (CBTL/CBGL), the cumulative number of correct classifications of the training/test sample within the training phase, is proposed to quantify the stability in the memorization-generalization process, while forgetting/mal-generalizing events (ForEvents/MgEvents), i.e., the misclassification of previously learned or generalized samples, are utilized to represent the uncertainty of sample regularity with respect to optimization dynamics. The effectiveness and robustness of the proposed approaches for mini-batch stochastic gradient descent (SGD) optimization are validated through sample-wise analyses. Further training/test sample selection applications show that the proposed measures, which share the unified computing procedure, could benefit both tasks. © The Author(s) 2024.

关键词： Forgetting events Memorization-generalization mechanism Network generalization Sample regularity Sample reweighting

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A Digital Twin for Telesurgery under Intermittent Communication

arXiv

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arXiv 2024年

作者： Wang, Junxiang Barragan, Juan Antonio Ishida, Hisashi Guo, Jingkai Ku, Yu-Chun Kazanzides, Peter Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States Robotics Institute Carnegie Mellon University PittsburghPA15213 United States

Telesurgery is an effective way to deliver service from expert surgeons to areas without immediate access to specialized resources. However, many of these areas, such as rural districts or battlefields, might be subject to different problems in communication, especially latency and intermittent periods of communication outage. This challenge motivates the use of a digital twin for the surgical system, where a simulation would mirror the robot hardware and surgical environment in the real world. The surgeon would then be able to interact with the digital twin during communication outage, followed by a recovery strategy on the real robot upon reestablishing communication. This paper builds the digital twin for the da Vinci surgical robot, with a buffering and replay strategy that reduces the mean task completion time by 23% when compared to the baseline, for a peg transfer task subject to intermittent communication outage. © 2024, CC BY.

关键词： Robotic surgery

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Multi-Scale Dynamic and Hierarchical Relationship Modeling for Facial Action Units Recognition

Multi-Scale Dynamic and Hierarchical Relationship Modeling f...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zihan Wang Siyang Song Cheng Luo Songhe Deng Weicheng Xie Linlin Shen Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Shenzhen Institute of Artificial Intelligence and Robotics for Society National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Universiry of Leicester Monash University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Human facial action units (AUs) are mutually related in a hierarchical manner, as not only they are associated with each other in both spatial and temporal domains but also AUs located in the same/close facial regions show stronger relationships than those of different facial regions. While none of existing approach thoroughly model such hi-erarchical inter-dependencies among AUs, this paper proposes to comprehensively model multi-scale AU-related dynamic and hierarchical spatiotemporal relationship among AUs for their occurrences recognition. Specifically, we first propose a novel multi-scale temporal differencing network with an adaptive weighting block to explicitly capture facial dynamics across frames at different spatial scales, which specifically considers the heterogeneity of range and mag-nitude in different AUs' activation. Then, a two-stage strategy is introduced to hierarchically model the relationship among AUs based on their spatial distribution (i.e., local and cross-region AU relationship modelling). Experimental results achieved on BP4D and DISFA show that our approach is the new state-of-the-art in the field of AU occurrence recognition. Our code is publicly available at https://***/CVI-SZU/MDHR.

关键词： Gold Adaptation models computer vision Graphical models Codes Adaptive systems Computational modeling

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Spatially Small-scale Approach-avoidance Behaviors Allow Learning-free Machine Inference of Object Preferences in Human Minds

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International Journal of Social robotics 2023年第6期15卷 999-1006页

作者： Huang, Tsung-Ren Chen, Tzu-Chun Lin, Ting-Yu Goh, Joshua O. S. Chang, Yu-Ling Yeh, Su-Ling Fu, Li-Chen Department of Psychology National Taiwan University No. 1 Sec. 4 Roosevelt Rd Taipei10617 Taiwan Graduate Institute of Brain and Mind Sciences National Taiwan University Taipei Taiwan Center for Artificial Intelligence and Advanced Robotics National Taiwan University Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Estimating human preference is an essential capability of a social robot. Such a machine Theory of Mind for others’ preferences is studied predominantly under the framework of inverse reinforcement learning, which however requires millions of approach-avoidance trajectories in a simulated grid world as training samples to achieve state-of-the-art results. Here, the present study explores the utility of taking spatially small-scale approach-avoidance behaviors in real life as predictive cues for efficiently estimating human preferences. Using a toy-playing scenario as an example (ΣN = 58 young and older adults), we found that a person’s subjectively reported and objectively inferred levels of attention and happiness could well predict the person’s subjective levels of toy preference. Importantly, attention and happiness can be rapidly estimated from gaze directions and emotional expressions by computer vision techniques in a learning-free manner. Compared with preference learning from spatially larger-scale movement behaviors, robot estimation of human preferences from these smaller-scale behavioral cues can be more efficient and generalizable to unlearned situations in real life. © 2023, The Author(s), under exclusive licence to Springer Nature B.V.

关键词： Human robot interaction

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