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arXiv 2025年

作者： Li, Chenzui Wu, Xi Liu, Junjia Teng, Tao Chen, Yiming Calinon, Sylvain Caldwell, Darwin Chen, Fei Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong Idiap Research Institute Martigny Switzerland Department of Advanced Robotics Istituto Italiano di Tecnologia Genoa16163 Italy

Humans are experts in collaborating with others physically by regulating compliance behaviors based on the perception of their partners’ states and the task requirements. Enabling robots to develop proficiency in human collaboration skills can facilitate more efficient human-robot collaboration (HRC). This paper introduces an innovative impedance regulation skill learning framework for achieving HRC in multiple physical collaborative tasks. The framework is designed to adjust the robot compliance to the human partner’s states while adhering to reference trajectories provided by human-human demonstrations. Specifically, electromyography (EMG) signals from human muscles are collected and analyzed to extract limb impedance, representing compliance behaviors during demonstrations. Human endpoint motions are captured and represented using a probabilistic learning method to create reference trajectories and corresponding impedance profiles. Meanwhile, an LSTM-based module is implemented to develop task-oriented impedance regulation policies by mapping the muscle synergistic contributions between two demonstrators. Finally, we propose a whole-body impedance controller for a human-like robot, coordinating joint outputs to achieve the desired impedance and reference trajectory during task execution. Experimental validation was conducted through a collaborative transportation task and two interactive Tai Chi pushing hands tasks, demonstrating superior performance from the perspective of interactive forces compared to a constant impedance control method. Copyright © 2025, The Authors. All rights reserved.

关键词： Electromyography

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Aerial Dockable Multirotor UAVs: Design, Control, and Flight Time Extension Through In-Flight Battery Replacement

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IEEE Access 2025年 13卷 96782-96799页

作者： Yeongin Song Hyunmin Kim Jeonghyun Byun Keun Park Murim Kim Seung Jae Lee Department of Mechanical Design and Robot Engineering Seoul National University of Science and Technology (SeoulTech) Seoul Republic of Korea Department of Aerospace Engineering Seoul National University Seoul Republic of Korea Human-Centered Robotics Research and Development Division Korea Institute of Robotics and Technology Convergence (KIRO) Pohang Republic of Korea

This paper presents the development and evaluation of the AirDock system, a novel aerial docking and in-flight battery exchange solution designed to extend the operational flight time of multirotor unmanned aerial vehicles (mUAVs). The proposed system enables seamless mid-flight battery replacement through an aerial docking mechanism and a rail-Car-based battery transfer and retrieval system. The AirDock platform utilizes a fully actuated multirotor design, allowing independent control of six degrees of freedom (DoF) motion to maintain a constant attitude during battery exchange operations, facilitating easy docking. In this research, a generalized control allocation algorithm is introduced to manage dynamic shifts in the center of mass (CoM) during inter-modular battery transportation, ensuring safe flight control and mitigating thruster overloading: a critical factor in avoiding flight failure. Both simulations and real-world experiments validate the system’s effectiveness, demonstrating stable aerial docking, reliable in-flight battery exchange performance, and extended flight durations for mission modules. These findings highlight the AirDock system’s potential to overcome the energy limitations of battery-powered UAVs, extending missions across various applications.

关键词： Batteries Propellers Propulsion Plugs Torque Hardware Robots Quadrotors Couplings Buildings

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The Immense Impact of Reverse Edges on Large Hierarchical Networks

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engineering 2024年第5期36卷 240-249页

作者： Haosen Cao Bin-Bin Hu Xiaoyu Mo Duxin Chen Jianxi Gao Ye Yuan Guanrong Chen Tamás Vicsek Xiaohong Guan Hai-Tao Zhang MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Intelligent Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Jiangsu Key Laboratory of Networked Collective Intelligence School of MathematicsSoutheast UniversityNanjing 210096China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Electronic Engineering City University of Hong KongHong Kong 999077China Department of Biological Physics Eötvös UniversityBudapest 1117Hungary MOE Key Laboratory for Intelligent Networks and Network Security School of Automation Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China

Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so *** structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social *** study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific *** surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved *** overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization *** finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these *** study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

关键词： Synchronizability Large hierarchical networks Reverse edges Information flows Complex networks

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Human-Humanoid Robots Cross-Embodiment Behavior-Skill Transfer Using Decomposed Adversarial Learning from Demonstration

arXiv

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arXiv 2024年

作者： Liu, Junjia Li, Zhuo Yu, Minghao Dong, Zhipeng Calinon, Sylvain Caldwell, Darwin Chen, Fei Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong Idiap Research Institute Martigny Switzerland Department of Advanced Robotics Istituto Italiano di Tecnologia Genoa Italy

Humanoid robots are envisioned as embodied intelligent agents capable of performing a wide range of human-level loco-manipulation tasks, particularly in scenarios requiring strenuous and repetitive labor. However, learning these skills is challenging due to the high degrees of freedom of humanoid robots, and collecting sufficient training data for humanoid is a laborious process. Given the rapid introduction of new humanoid platforms, a cross-embodiment framework that allows generalizable skill transfer is becoming increasingly critical. To address this, we propose a transferable framework that reduces the data bottleneck by using a unified digital human model as a common prototype and bypassing the need for re-training on every new robot platform. The model learns behavior primitives from human demonstrations through adversarial imitation, and the complex robot structures are decomposed into functional components, each trained independently and dynamically coordinated. Task generalization is achieved through a human-object interaction graph, and skills are transferred to different robots via embodimentspecific kinematic motion retargeting and dynamic fine-tuning. Our framework is validated on five humanoid robots with diverse configurations, demonstrating stable loco-manipulation and highlighting its effectiveness in reducing data requirements and increasing the efficiency of skill transfer across platforms. Copyright © 2024, The Authors. All rights reserved.

关键词： Anthropomorphic robots

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Image dehazing with scattering coefficient estimation using cnn-based image regression

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Journal of institute of Control, robotics and Systems 2021年第11期27卷 890-896页

作者： Chung, Won Young Kim, Sun Young Park, Chan Gook Kang, Chang Ho Kumoh National Institute of Technology Korea Republic of Department of Mechanical Convergence System Engineering Kunsan National University Korea Republic of Department of Aerospace Engineering and Automation and System Research Institute Seoul National University Korea Republic of

The estimation of the scattering coefficient in depth image-based dehazing is of paramount importance. Since scattering coefficients are used to estimate the transmission image for dehazing, the optimal scattering coefficients for effective dehazing must be obtained depending on the level of haze and fog generation. In this study, we performed a CNN-based image regression to obtain the optimal scattering coefficients for each image with fog and haze. A three-channel image was used as the input data, and the learning was performed with approximately 2,000 labeled synthetic haze and fog datasets. Subsequently, the transmission image was estimated using the scattering coefficient obtained for the input image through the learned model, and the depth image was obtained through the LiDAR point cloud projection for performing the dehazing. This paper presents a qualitative and quantitative comparison of the results obtained using the proposed dehazing technique with those obtained using the existing dehazing algorithms. © ICROS 2021.

关键词： Fog

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Detecting Heel Strike and toe off Events Using Kinematic Methods and LSTM Models

arXiv

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arXiv 2025年

作者： Zhang, Longbin Wu, Tsung-Lin Sidarta, Ananda Yan, Xiaoyue Jatesiktat, Prayook Yang, Kailun Ang, Wei Tech School of Robotics Hunan University Changsha China School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Rehabilitation Research Institute of Singapore Lee Kong Chian School of Medicine Nanyang Technological University Singapore School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore

Accurate gait event detection is crucial for gait analysis, rehabilitation, and assistive technology, particularly in exoskeleton control, where precise identification of stance and swing phases is essential. This study evaluated the performance of seven kinematics-based methods and a Long Short-Term Memory (LSTM) model for detecting heel strike and toe-off events across 4363 gait cycles from 588 able-bodied subjects. The results indicated that while the Zeni et al. method achieved the highest accuracy among kinematics-based approaches, other methods exhibited systematic biases or required dataset-specific tuning. The LSTM model performed comparably to Zeni et al., providing a data-driven alternative without systematic bias. These findings highlight the potential of deep learning-based approaches for gait event detection while emphasizing the need for further validation in clinical populations and across diverse gait conditions. Future research will explore the generalizability of these methods in pathological populations, such as individuals with post-stroke conditions and knee osteoarthritis, as well as their robustness across varied gait conditions and data collection settings to enhance their applicability in rehabilitation and exoskeleton control. Copyright © 2025, The Authors. All rights reserved.

关键词： Exoskeleton (robotics)

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Heuristic Online Path Planning for Mobile Air Purification and Disinfection Robot In Regulating Indoor Air Quality

Heuristic Online Path Planning for Mobile Air Purification a...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Zhong, Qingnan Chen, Kai Duan, Jianghua Ma, Jun Hkust Shenzhen-Hong Kong Collaborative Innovation Research Institute Robotics and Human Machine Interface Laboratory Futian China The Hong Kong University of Science and Technology Department of Mechanical and Aerospace Engineering Hong Kong Robotics and Autonomous Systems Thrust Guangzhou China The Hong Kong University of Science and Technology Division of Emerging Interdisciplinary Areas Hong Kong Hkust Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian China

ISBN: (纸本)9798350325706

In light of past epidemics, people have become more aware of the possibility of viruses on object surfaces and in the air. As a result, there is an increasing demand for robots capable of autonomously disinfecting surfaces and purifying the air. This paper focuses on a mobile robot equipped with air purification and UV-C LED disinfection to combat airborne pathogens and environmental pollution. Through the onboard sensors, the proposed algorithm uses gaussian process model to predict the high potential air pollution area in real-time without the requirement of a distributed array of sensors, and it avoids environmental modifications. Then we propose the hybrid artificial potential field (HAPF) by integrating the predicted result with the artificial potential field method, to generate a heuristic path that can navigate the robot to the affected areas with obstacle avoidance. Experimental testing in simulated and real-world environments demonstrates the effectiveness of the proposed approach in rapidly planning purification paths, marking significant progress in automated air purification, and disinfection technology. © 2023 IEEE.

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Dynamics of triboelectric nanogenerators: A review

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International Journal of mechanical System Dynamics 2022年第4期2卷 311-324页

作者： Guoqiang Xu Chuanyang Li Chaojie Chen Jingjing Fu Tingting Hou Yunlong Zi Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong Kong SARChina Shenzhen Research Institute The Chinese University of Hong KongGuangdongShenzhenChina HKUST Shenzhen‐Hong Kong Collaborative Innovation Research Institute GuangdongShenzhenChina Thrust of Sustainable Energy and Environment The Hong Kong University of Science and Technology(Guangzhou)GuangdongGuangzhouChina Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and TechnologyHong Kong SARChina

Triboelectric nanogenerators(TENGs)represent a promising next‐generation renewable energy *** have become increasingly popular for harvesting vibration energy in the environment due to their advantages of lightweight,broad range of material choices,low cost,and no ***,issues such as input force irregularity,working bandwidth,efficiency calculation,and dynamic modeling hinder the use of TENGs in industrial or practical *** this paper,the modeling process of the dynamical system of a TENG is reviewed from the perspective of energy *** addition,this paper reviews the main contributions made in recent years to achieve optimized output based on springs,magnetic forces,and pendulums,and introduces different ways to increase the bandwidth of ***,the main problems of TENGs in the process of harvesting vibration energy are *** review may serve as a practical reference for methods to convert irregular mechanical input sources into optimized output performance toward the commercialization of TENGs.

关键词： triboelectric nanogenerator vibration energy harvesting electromechanical coupling model broad bandwidth

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Ground Contact Parameter Estimation Guided Gait Planning for Hexapod Robots

Ground Contact Parameter Estimation Guided Gait Planning for...

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IEEE International Conference on robotics and Biomimetics

作者： Guiyu Dong Ripeng Qin Liangliang Han Jiawei Chen Kun Xu Xilun Ding Robotics Institute School of Mechanical Engineering and Automation Beihang University Beijing China Institute of Aerospace System Engineering Shanghai Shanghai China

ISBN: (纸本)9781665481106

Environment sensing for legged robots can improve their mobility performance. A foot-ground contact model can be used to evaluate the properties of the robot's contact with the environment. A contact parameter estimator based on artificial neural networks was developed to guide the gait planning of a hexapod robot so that it can achieve higher mobility efficiency. This contact parameter estimator can use acceleration, velocity, and contact force data as inputs and output contact parameters. The estimator avoided using deformations as input which are difficult to measure. Meanwhile, the robot's cost of transport with different contact parameters is tested and recorded. Accordingly, the hexapod robot was guided to choose a better gait shape between cycloid and rectangular. The simulation proved that changes in the gait shape according to the contact parameters can reduce the hexapod's cost of transport.

关键词： Robot motion Costs Parameter estimation Shape Force Robot sensing systems Real-time systems

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Decentralized Deadlock-free Trajectory Planning for Quadrotor Swarm in Obstacle-rich Environments

Decentralized Deadlock-free Trajectory Planning for Quadroto...

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IEEE International Conference on robotics and automation (ICRA)

作者： Jungwon Park Inkyu Jang H. Jin Kim Department of Mechanical and Aerospace Engineering Seoul National University (SNU) and Automation and Systems Research Institute (ASRI) Seoul South Korea

This paper presents a decentralized multi-agent trajectory planning (MATP) algorithm that guarantees to generate a safe, deadlock-free trajectory in an obstacle-rich environment under a limited communication range. The proposed algorithm utilizes a grid-based multi-agent path planning (MAPP) algorithm for deadlock resolution, and we introduce the subgoal optimization method to make the agent converge to the waypoint generated from the MAPP without deadlock. In addition, the proposed algorithm ensures the feasibility of the optimization problem and collision avoidance by adopting a linear safe corridor (LSC). We verify that the proposed algorithm does not cause a deadlock in both random forests and dense mazes regardless of communication range, and it outperforms our previous work in flight time and distance. We validate the proposed algorithm through a hardware demonstration with ten quadrotors.

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