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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Cecilia De Vicariis Ekaterina Ivanova Vittorio Sanguineti Etienne Burdet Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Imperial College of Science Technology and Medicine London UK School of Electronic Engineering and Computer Science Queen Mary University of London UK

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

We refer to joint action as any social interaction where two or more people coordinate their actions in space and time to bring about a change in the environment. We are constantly involved in joint actions in our daily life - lifting a piece of furniture, playing team sport, and during a rehabilitation session. Here, we aim at characterizing emergent coordination in pairs of participants mechanically coupled through a virtual elastic band while moving the hand through multiple via-points. We assessed the level of coordination in terms of temporal synchronization and trajectory similarity metrics. Overall, we found that participants changed their behaviour when mechani-cally coupled toward coordination, both in space and time, compared to their solo performances. However, coordination rapidly disappeared when the mechanical coupling was removed. These results provide insights into the understanding of the mecha-nisms underlying the development of joint coordination while haptically connected.

关键词： Measurement Couplings Robot kinematics Biomechatronics Robot sensing systems Trajectory Haptic interfaces Synchronization Sports

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Distribution Aligned Diffusion and Prototype-guided network for Unsupervised Domain Adaptive Segmentation

arXiv

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arXiv 2023年

作者： Zhou, Haipeng Zhu, Lei Zhou, Yuyin School of Computer Science and Technology Xi’an Jiaotong University China Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology Guangzhou China University of California Santa Cruz United States

The Diffusion Probabilistic Model (DPM) has emerged as a highly effective generative model in the field of computer vision. Its intermediate latent vectors offer rich semantic information, making it an attractive option for various downstream tasks such as segmentation and detection. In order to explore its potential further, we have taken a step forward and considered a more complex scenario in the medical image domain, specifically, under an unsupervised adaptation condition. To this end, we propose a Diffusion-based and Prototype-guided network (DP-Net) for unsupervised domain adaptive segmentation. Concretely, our DP-Net consists of two stages: 1) Distribution Aligned Diffusion (DADiff), which involves training a domain discriminator to minimize the difference between the intermediate features generated by the DPM, thereby aligning the inter-domain distribution;and 2) Prototype-guided Consistency Learning (PCL), which utilizes feature centroids as prototypes and applies a prototype-guided loss to ensure that the segmentor learns consistent content from both source and target domains. Our approach is evaluated on fundus datasets through a series of experiments, which demonstrate that the performance of the proposed method is reliable and outperforms state-of-the-art methods. Our work presents a promising direction for using DPM in complex medical image scenarios, opening up new possibilities for further research in medical imaging. Copyright © 2023, The Authors. All rights reserved.

关键词： Diffusion

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Creating Trustworthy AI for UAS using Labeled Backchained Behavior Trees

Creating Trustworthy AI for UAS using Labeled Backchained Be...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Petter Ögren Jens Alfredson Robotics Perception and Learning Lab. School of Electrical Engineering and Computer Science Royal Institute of Technology (KTH) Stockholm Sweden Saab Aeronautics Sweden

Unmanned Aerial Systems (UAS) have the potential to provide cost effective solutions to many problems, but their control systems need to be safe and trustworthy in order to realize this potential. In this paper we show how behavior trees (BTs), created using backward chaining and using a particular way of labelling subtrees, can be used to meet the requirements of trustworthy autonomy described in a US air force (USAF) report. Behavior Trees represent a modular, reactive and transparent way of structuring a control system that is receiving increasing interest in the UAS community. While their safety and efficiency have been investigated in prior research, their connection to trustworthy autonomy has not been explored. A set of guidelines for trustworthy autonomy, taken from a USAF report, include items such as: being similar to how humans parse problems, being able to explain its reasoning in a concise way, and being able to be visualized at different levels of resolution. We propose a new way of deriving explanations that conform to these guidelines, using a particular labeling of subtrees in the BT combined with a structured design methodology called backward chaining. The proposed approach is illustrated in a detailed example.

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A 4-channel acoustic sensor system for sound source detection of the service robot

A 4-channel acoustic sensor system for sound source detectio...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Jiaxin Hu Bo Lu Jinlin Liu Tiancheng Zhou Xinjian Fan Xiaoang Liu Dianbiao Dong Long Chen School of Mechanical Engineering Northwestern Polytechnical University Xi’an China Robotics and Microsystems Center School of Mechanical and Electric Engineering Soochow University Suzhou China School of Computer Science & Technology Huazhong University of Science and Technology Wuhan China Tianjin Key Laboratory of Power Transmission and Safety Technology for New Energy Vehicles School of Mechanical Engineering Hebei University of Technology Tianjin China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

Constrained by the physical geometry of microphone arrays on the service robot, there exist limitations on both the minimum and maximum frequency bounds along with the coverage area. While the mobility of a microphone array carried by a robot offers the potential for a broader operational frequency range and a more comprehensive perspective, achieving this necessitates a practically more extensive and densely packed array configuration. This is often accomplished by employing non-synchronous measurements in beamforming. Here, we propose a novel approach for locating broadband sound sources using an asynchronous measurement technique that employs Laplacian-enhanced low-rank tensor completion. Our technique analyzes the tensor data structure within broadband signals and incorporates an alternating-direction approach based on multiplier optimization. Through the simulation study using only four physical channels, our Laplacian-enhanced algorithm has demonstrated the ability to generate a highly accurate sound map capturing distinct speech signal sources, thus providing a superior global view compared to the alternative convex surrogate. This advancement holds substantial promise for applications in which precise localization of broadband sound sources is paramount.

关键词： Location awareness Tensors Service robots Simulation Prototypes Microphone arrays Real-time systems

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A Stable Guidance Method for Teleoperation-based Robot Learning from Demonstration

A Stable Guidance Method for Teleoperation-based Robot Learn...

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IEEE International Conference on Automation science and Engineering (CASE)

作者： Weiyong Si Zhehao Jin Zhenyu Lu Ning Wang Chenguang Yang School of Computer Science and Electronic Engineering at the University of Essex Colchester UK College of Information Engineering Zhejiang University of Technology Hangzhou China Bristol Robotics Lab at the University of the West of England Bristol UK Department of Computer Science University of Liverpool Liverpool UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Robot learning from teleoperation-based demonstration has been proven to be an effective method for robot skills learning. However, the human demonstration process is mentally and physically challenging due to the dexterous and continuous interaction control. Especially, for contact-rich tasks human operators need to control the pose and contact force between the end-effector and the environment, only based on visual and haptic feedback. To tackle the problem, this work studies a stable guidance method for teleoperation-based demonstration to improve the experience and performance of human demonstration and reduce the workload of human operators. The guidance design is based on a dynamic system-based imitation learning model, which is achieved by human demonstration. The virtual guidance force will decrease as the virtual guidance’s iterative learning. The efficiency of human demonstration is improved, and human operators’ physical and mental workload is reduced. Two typical experiments, robot-assisted polishing and robot-assisted ultrasound scanning, are conducted to validate the performance of the guidance method.

关键词： Visualization Ultrasonic imaging Imitation learning Force Dynamics Process control Robot learning End effectors Haptic interfaces Iterative methods

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HairDiffusion: vivid multi-colored hair editing via latent diffusion 24

HairDiffusion: vivid multi-colored hair editing via latent d...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yu Zeng Yang Zhang Jiachen Liu Linlin Shen Kaijun Deng Weizhao He Jinbao Wang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University and Shenzhen Institute of Artificial Intelligence and Robotics for Society and National Engineering Laboratory for Big Data System Computing Technology Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University and Guangdong Provincial Key Laboratory of Intelligent Information Processing

ISBN: (纸本)9798331314385

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images.

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Fourier Boundary Features Network with Wider Catchers for Glass Segmentation

arXiv

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arXiv 2024年

作者： Qin, Xiaolin Liu, Jiacen Wang, Qianlei Zhang, Shaolin Zhu, Fei Yi, Zhang Chengdu Institute of Computer Applications Chinese Academy of Sciences Chengdu610213 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences 999077 Hong Kong School of Computer Science Sichuan University Chengdu610065 China

Glass largely blurs the boundary between the real world and the reflection. The special transmittance and reflectance quality have confused the semantic tasks related to machine vision. Therefore, how to clear the boundary built by glass, and avoid over-capturing features as false positive information in deep structure, matters for constraining the segmentation of reflection surface and penetrating glass. We proposed the Fourier Boundary Features Network with Wider Catchers (FBWC), which might be the first attempt to utilize sufficiently wide horizontal shallow branches without vertical deepening for guiding the fine granularity segmentation boundary through primary glass semantic information. Specifically, we designed the Wider Coarse-Catchers (WCC) for anchoring large area segmentation and reducing excessive extraction from a structural perspective. We embed fine-grained features by Cross Transpose Attention (CTA), which is introduced to avoid the incomplete area within the boundary caused by reflection noise. For excavating glass features and balancing high-low layers context, a learnable Fourier Convolution Controller (FCC) is proposed to regulate information integration robustly. The proposed method has been validated on three different public glass segmentation datasets. Experimental results reveal that the proposed method yields better segmentation performance compared with the state-of-the-art (SOTA) methods in glass image segmentation. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Online Learning in Blockchain-based Energy Trading Systems 24

Online Learning in Blockchain-based Energy Trading Systems

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2024 International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, MobiHoc 2024

作者： Liu, Yunshu Cheung, Man Hon Huang, Jianwei Pillar of Engineering Systems and Design Singapore University of Technology and Design Singapore Singapore Department of Computer Science City University of Hong Kong Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network CSIJRI Joint Research Centre on Smart Energy Storage The Chinese University of HongKong Guangdong Shenzhen518172 China

ISBN: (纸本)9798400705212

In this paper, we consider a blockchain-based energy trading (BBET) system with the proof-of-stake (PoS) protocol. The system designer aims to minimize system cost by considering the prosumers’ strategic token allocation between blockchain staking and energy purchase for their applications. This is challenging as the system designer does not know prosumers’ private information of impatience levels towards different applications. To this end, we propose an online learning mechanism (OLM), which includes incentive mechanisms to guide both prosumers’ private information reporting and staking decisions in two phases. In the exploration phase, we design a randomized staking reward to encourage prosumers’ truthful reporting of their private information for the learning of impatience level distributions. Based on the threshold structure of the prosumers’ equilibrium staking strategies, in the exploitation phase, we propose a learning-error-based reward to minimize the system cost considering the finite-sample bias. By characterizing the optimal exploration duration, we prove that OLM achieves an asymptotic zero-regret against the complete information benchmark, with the regret bounded by O(3√lnT/T) when operating for T time slots. We implement the corresponding smart contract in Ethereum to demonstrate the feasibility of our approach. Experiment results show that our mechanism reduces regret by an average of 74% compared to the state-of-art mechanism. © 2024 Copyright held by the owner/author(s).

关键词： Blockchain

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AgileVision Academy: Indoor Drone Navigation and Obstacle Avoidance

AgileVision Academy: Indoor Drone Navigation and Obstacle Av...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Haolong Jiang Lingdan Gu Jiayu Wang Xiao Lan Qinyao Liu Fanxin Wang Department of Electrical and Electronic Engineering School of Advanced Technology Xi’an Jiaotong Liverpool University University of Liverpool Suzhou China Department of Intelligence Robotics Engineering With Contemporary Entrepreneurialism School of Robotics Xi'an Jiaotong Liverpool University Suzhou China Department of Information and Computer Science School of Advanced Technology Xi’an Jiaotong Liverpool University Suzhou China Department of Electrical and Electronic Engineering School of Advanced Technology Xi’an Jiaotong Liverpool University Suzhou China Department of Mechatronics and Robotics School of Advanced Technology Xi’an Jiaotong Liverpool University Suzhou China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

Navigating cluttered indoor environments presents a significant challenge for aerial robots, requiring agility, speed, and a high level of reliability to avoid collisions. This project aims to address this challenge by developing advanced algorithms for lightweight drones, enabling them to reach target location and carrying out delivery efficiently and safely. Utilizing camera data to map and avoid obstacles, we create a system that not only maps and perceives the environment but also navigates its path to the destination. Included in the educational content, this project demonstrates the effectiveness of the technology through practical applications and real-world testing, helping the next generation of engineers accumulate technical experience and broaden their understanding of drone navigation algorithms and applications in system integration. It has a milestone impact on education and development of the drone field.

关键词： Accuracy Navigation Trajectory tracking Heuristic algorithms Education Indoor environment Safety Nonlinear dynamical systems Collision avoidance Drones

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POMDP-Guided Active Force-Based Search for Robotic Insertion

arXiv

引用

arXiv 2024年

作者： Wang, Chen Luo, Haoxiang Zhang, Kun Chen, Hua Pan, Jia Zhang, Wei School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Department of Computer Science The University of Hong Kong Hong Kong Cheng Kar-Shun Robotics Institute Hong Kong University of Science and Technology Clear Water Bay Hong Kong

In robotic insertion tasks where the uncertainty exceeds the allowable tolerance, a good search strategy is essential for successful insertion and significantly influences efficiency. The commonly used blind search method is time-consuming and does not exploit the rich contact information. In this paper, we propose a novel search strategy that actively utilizes the information contained in the contact configuration and shows high efficiency. In particular, we formulate this problem as a Partially Observable Markov Decision Process (POMDP) with carefully designed primitives based on an in-depth analysis of the contact configuration's static stability. From the formulated POMDP, we can derive a novel search strategy. Thanks to its simplicity, this search strategy can be incorporated into a Finite-State-Machine (FSM) controller. The behaviors of the FSM controller are realized through a low-level Cartesian Impedance Controller. Our method is based purely on the robot's proprioceptive sensing and does not need visual or tactile sensors. To evaluate the effectiveness of our proposed strategy and control framework, we conduct extensive comparison experiments in simulation, where we compare our method with the baseline approach. The results demonstrate that our proposed method achieves a higher success rate with a shorter search time and search trajectory length compared to the baseline method. Additionally, we show that our method is robust to various initial displacement errors. Copyright © 2024, The Authors. All rights reserved.

关键词： robotics

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