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A trajectory-level robot skill learning method based on fuzzy systems

science China(Information sciences) 2025年第5期68卷 38-52页

作者： Chao ZENG Zhen DENG Boyang TI Yongsheng GAO Department of Computer Science University of Liverpool School of Mechanical Engineering and Automation Fuzhou University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics and System Harbin Institute of Technology

Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning model that can comprehensively satisfy multiple requirements,such as computational complexity,modeling accuracy,trajectory smoothness,and robustness,is still ***,this work aims to provide such a model by employing fuzzy ***,we introduce an LfD model named Takagi-Sugeno-Kang fuzzy system-based movement primitives(TSKFMPs),which exploits the advantages of the fuzzy theory for effective robotic imitation learning of human *** work formulates the TSK fuzzy system and gradient descent(GD) as imitation learning models,leveraging recent advancements in GD-based optimization for fuzzy *** study takes a two-step strategy.(ⅰ) The input-output relationships of the model are established using TSK fuzzy systems based on demonstration *** this way,the skill is encoded by the model parameter in the latent space.(ⅱ) GD is used to optimize the model parameter to increase the modeling accuracy and trajectory *** further explain how learned trajectories are adapted to new task scenarios through local *** conduct multiple tests using an open dataset to validate our method,and the results demonstrate performance comparable with those of other ***,we implement it in a real-world case study.

关键词： robot skill learning movement primitives TSK fuzzy systems gradient descent robotics

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A Hierarchical Progressive Perception System for Autonomous Luggage Trolley Collection

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IEEE Transactions on Automation science and engineering 2025年 22卷 14484-14497页

作者： Sun, Zhirui Zhang, Zhe Zhao, Jieting Ye, Hanjing Wang, Jiankun Southern University of Science and Technology Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Shenzhen518055 China Southern University of Science and Technology Jiaxing Research Institute Jiaxing314000 China Southern University of Science and Technology Shenzhen Key Laboratory of Robotics and Computer Vision Department of Electronic and Electrical Engineering Shenzhen518055 China

Advancements in intelligent vehicle technologies and autonomous driving are now used for tasks like collecting luggage trolleys at airports. The robotic autonomous luggage trolley collection system employs robots to gather and transport scattered luggage trolleys. However, existing methods for detecting and locating luggage trolleys often fail when they are not fully visible. To address this, we introduce the hierarchical progressive perception system, which enhances the detection and localization of luggage trolleys under partial occlusion. The proposed system integrates the hierarchical process structure and progressive perception strategy. This innovative structure processes the luggage trolley’s position and orientation separately. It can accurately determine the luggage trolley’s position with just one well-detected keypoint and estimate its orientation when partially occluded. Once the luggage trolley’s initial pose is detected, the progressive perception strategy continuously refines this information until the robot begins grasping. The proposed system only needs RGB images for labeling and training, eliminating the need for complex data collection and annotation. The experiments on detection and localization demonstrate that the proposed system is more reliable under partial occlusion compared to existing methods. Its effectiveness and robustness have also been confirmed through practical tests in actual luggage trolley collection tasks. © 2004-2012 IEEE.

关键词： Intelligent robots

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Ankle Exoskeleton Control via Data-Driven Gait Estimation for Walking, Running, and Inclines

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IEEE robotics and Automation Letters 2025年第6期10卷 5855-5862页

作者： Shetty, Pavan R. Menezes, Jayston A. Song, Seungmoon Young, Aaron J. Shepherd, Max K. Northeastern University Department of Mechanical Engineering BostonMA02115 United States Northeastern University Department of Mechanical Engineering Institute for Experiential Robotics BostonMA02115 United States Georgia Institute of Technology Woodruff School of Mechanical Engineering Institute for Robotics and Intelligent Machines AtlantaGA30332-0405 United States Northeastern University Department of Mechanical Engineering Institute for Experiential Robotics Department of Physical Therapy Movement and Rehabilitation Science BostonMA02115 United States

Ankle exoskeletons have the potential to augment mobility, but control strategies have largely failed to seamlessly adapt to changes in the locomotion task. Here, we introduce a multi-headed network that predicts gait speed, ground incline, stance/swing transitions, and percent stance. These predictions are mapped to exoskeleton torque using typical biological torques as a guide. The model was trained on 9 subjects walking/jogging for 12 minutes across a range of speeds and inclines. The controller was validated on 4 subjects, and achieved stance phase prediction error of 3.4% across a range of speeds and inclines, both inside and outside the training set distribution. A secondary analysis showed similar accuracy could have been obtained with only 10% of the collected data, suggesting researchers may need fewer total strides of training data, provided the data is sufficiently diverse across users and tasks. Metabolic cost was improved during running compared to wearing the exoskeleton powered off, but was beneficial for only one subject during level walking and ramp ascent when compared to no exoskeleton. Overall, our controller smoothly adapted to time-varying inclines and walking/jogging speeds, and achieved high accuracy with a reduced training dataset, though larger torque magnitudes may be required to see metabolic benefit. © 2016 IEEE.

关键词： Exoskeleton (robotics)

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Heat Transfer Area Optimization for Heat Exchanger System

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Computer Modeling in engineering & sciences 2025年第4期143卷 335-349页

作者： Yu-Cheng Liao Fu-I Chou Po-Yuan Yang Jyh-Horng Chou Department of Electrical Engineering National Kaohsiung University of Science and TechnologyKaohsiung807Taiwan Department of Intelligent Robotics National Pingtung UniversityPingtung900Taiwan Department of Mechanical and Computer-Aided Engineering Feng Chia UniversityTaichung407Taiwan

This paper presents an allowable-tolerance-based group search optimization(AT-GSO),which combines the robust GSO(R-GSO)and the external quality design planning of the Taguchi ***-GSO algorithm is used to optimize the heat transfer area of the heat exchanger *** R-GSO algorithm integrates the GSO algorithm with the Taguchi method,utilizing the Taguchi method to determine the optimal producer in each iteration of the GSO algorithm to strengthen the robustness of the search process and the ability to find the global *** conventional parameter design optimization,it is typically assumed that the designed parameters can be applied accurately and consistently throughout ***,for systems that are sensitive to changes in design parameters,even minor inaccuracies can substantially reduce overall system ***,the permissible variations of the design parameters are considered in the tolerance-optimized design to ensure the robustness of the *** optimized design of the heat exchanger system assumes that the system’s operating temperature parameters are ***,fixing the systemoperating temperature parameters at a constant value is *** paper assumes that the system operating temperature parameters have an uncertainty error when optimizing the heat transfer area of the heat exchanger *** results show that the AT-GSO algorithm optimizes the heat exchanger system and finds the optimal operating temperature in the absence of tolerance and under three tolerance conditions.

关键词： Heat exchanger system allowable tolerance-based group search optimization Taguchi method tolerance design

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A Non-Energized Robotic Hand Using Locking Mechanism and Silicone Belts for Grasping Heavy Agricultural Products

A Non-Energized Robotic Hand Using Locking Mechanism and Sil...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Kamei, Tetsuhiro Wang, Zhongkui Ritsumeikan University Robotics Course of the Graduate School of Science and Engineering Kusatsu Japan Ritsumeikan University Department of Robotics Kusatsu Japan

ISBN: (纸本)9798331531614

This paper presents a robotic hand that utilizes both a locking mechanism and the elasticity of silicone belts to handle heavy agricultural products, such as radishes, addressing the labor shortage in fruit selection facilities caused by the declining birthrate and aging population. When grasping heavy agricultural products, significant force and stability are required to securely lift and transport the product. The proposed robotic hand combines a locking mechanism with the inherent elastic force of silicone belts, eliminating the need for active actuators such as motors or air compressors. The locking mechanism is embedded within the joints of the hand, allowing it to open and close through the combined effect of the silicone belts' elasticity and interaction with the environment. First, the effectiveness of this combined approach was validated through experiments involving the handling of heavy vegetables, including radishes and other similarly weighted objects. Next, the characteristics of the selected silicone material were measured, analyzed, and incorporated into the design. Finally, grasping experiments were conducted using the proposed hand, which leverages both the locking mechanism and the elasticity of silicone belts. The results indicate that the proposed robotic hand, which integrates both a locking mechanism and silicone belts, can reliably and stably grasp heavy agricultural products. © 2025 IEEE.

关键词： Robotic arms

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FoundationGrasp: Generalizable Task-Oriented Grasping With Foundation Models

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IEEE Transactions on Automation science and engineering 2025年 22卷 12418-12435页

作者： Tang, Chao Huang, Dehao Dong, Wenlong Xu, Ruinian Zhang, Hong Southern University of Science and Technology Shenzhen Key Laboratory of Robotics and Computer Vision Department of Electronic and Electrical Engineering Shenzhen518055 China Institute for Robotics and Intelligent Machines Georgia Institute of Technology AtlantaGA30332 United States

Task-oriented grasping (TOG), which refers to synthesizing grasps on an object that are configurationally compatible with the downstream manipulation task, is the first milestone towards tool manipulation. Analogous to the activation of two brain regions responsible for semantic and geometric reasoning during cognitive processes, modeling the intricate relationship between objects, tasks, and grasps neces-sitates rich semantic and geometric prior knowledge about these elements. Existing methods typically restrict the prior knowledge to a closed-set scope, limiting their generalization to novel objects and tasks out of the training set. To addresssuch a limitation, we propose FoundationGrasp, a foundationmodel-based TOG framework that leverages the open-ended knowledge from foundation models to learn generalizable TOG skills. Extensive experiments are conducted on the contributed Language and Vision Augmented TaskGrasp (LaViA-TaskGrasp)dataset, demonstrating the superiority of FoundationGrasp over existing methods when generalizing to novel object instances,object classes, and tasks out of the training set. Furthermore,the effectiveness of FoundationGrasp is validated in real-robot grasping and manipulation experiments on a 7-DoF robotic arm. © 2004-2012 IEEE.

关键词： Robotic arms

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Preferred Jacobian Differentiation and Direct Collocation Methods for an Efficient and Accurate Walker Gait Optimization

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International Journal of Control, Automation and Systems 2025年第5期23卷 1551-1562页

作者： Knisley, Veronica Cho, Kwonseung Lee, Kang-Woo Hur, Pilwon Southwest Research Institute San AntonioTX78238 United States Department of Mechanical and Robotics Engineering Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of

Devices based on walker robotics research sometimes require a reference trajectory for the control systems of the devices to track. One valuable class of methods used to find an optimal reference trajectory is direct collocation, but even after selecting a method like direct collocation, several optimization design decisions remain. In order to determine the most desirable optimization settings, 600 optimizations were performed for the trajectory of a two degree-of-freedom (DOF) compass gait walker, and 200 optimizations were performed for a five-DOF link walker. These runs evaluated various combinations of optimization settings, including: numerical vs. symbolic vs. automatic differentiation;trapezoidal vs. Hermite Simpson collocation;numerical vs. symbolic calculation of joint accelerations;and inclusion or exclusion of joint accelerations in the decision variables. The different generated gaits were then compared in terms of computational efficiency and accuracy. The results showed that including joint accelerations as decision variables eliminated alternative gaits but increased computational complexity and variability. Symbolic acceleration evaluation was preferable when automatic differentiation was excluded. Automatic differentiation was shown to be significantly faster than the other two differentiation methods for both walking models. In addition, Hermite-Simpson collocation, although slower than trapezoidal, was the more accurate of the two approaches. These results can be applied to the derivation of optimal reference joint trajectories in future robotics applications. © ICROS, KIEE and Springer 2025.

关键词： NP-hard

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A deep learning-based model for automated STN localization using local field potentials in Parkinson’s disease

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Soft Computing 2025年第4期29卷 2343-2362页

作者： Hosny, Mohamed Naeem, Mohamed A. Zhu, Minwei Gao, Wenpeng Elshenhab, Ahmed M. Fu, Yili Department of Electrical Engineering Benha Faculty of Engineering Benha University Benha Egypt State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150080 China Department of Mechatronics and Robotics Engineering Faculty of Engineering South Valley University Qena Egypt First Affiliated Hospital Harbin Medical University Harbin150001 China School of Life Science and Technology Harbin Institute of Technology Harbin150080 China Department of Mathematics Faculty of Science Mansoura University Mansoura35516 Egypt

Accurate identification of the subthalamic nucleus (STN) borders is time-consuming, relying heavily on the neurosurgeon expertise in manually interpreting the electrophysiological signals. Local field potentials (LFPs) have garnered imperative attention due to their strong correlation with the STN. However, existing detection models often face challenges with high computational complexity, hyperparameter optimization and lack of explainability, making them unreliable for clinicians. Therefore, this study introduces an explanatory framework using convolutional neural networks (CNN) for detecting the STN region from LFPs. Continuous wavelet transform is employed to convert LFPs signals into scalogram images, which are then processed by sixteen CNN models. We evaluated our framework by examining the impact of various limiting factors on the classification performance, including model size, learning rate (LR), optimizers and data scaling. Deep features are extracted from the top-performing CNN architectures to capture rich representations of the scalograms. These features are then fused and classified using k-nearest neighbour algorithm. Gradient-weighted class activation mapping is used to explain the decisions made by the proposed model. Our approach achieved an accuracy of 99.61%, outperforming individual CNN models for STN localization. The experimental results revealed that CNN models, embedded with additional hyperparameters and layers, generally outperformed smaller models. Besides, low LR significantly enhanced the performance compared to high LR. Moreover, features extracted from untuned networks produced lower performance than tuned networks. The proposed system could revolutionize deep brain stimulation surgery by increasing efficiency and reducing reliance on clinician expertise for STN detection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Wavelet transforms

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Aggressive and robust low-level control and trajectory tracking for quadrotors with deep reinforcement learning

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Neural Computing and Applications 2025年第3期37卷 1223-1240页

作者： Chen, Shiyu Li, Yanjie Lou, Yunjiang Lin, Ke Department of Control Science and Engineering Harbin Institute of Technology Shenzhen Shenzhen518055 China Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen518055 China

Executing accurate trajectory tracking tasks using a high-performance low-level controller is crucial for quadrotors to be applied in various scenarios, especially those involving uncertain disturbances. However, due to the uncertainties in disturbed environments, developing effective low-level controllers with traditional model-based control schemes is challenging. This paper presents an aggressive and robust reinforcement learning (RL)-based low-level control policy for quadrotors. The policy maps the observed quadrotor state directly to motor thrust commands, without requiring the quadrotor dynamics. Additionally, a trajectory generation pipeline is developed to improve the accuracy of trajectory tracking tasks based on differential flatness. With the learned low-level control policy, extensive simulations and real-world experiments are implemented to validate the performance of the policy. The results indicate that our RL-based low-level control policy outperforms traditional proportional–integral–derivative (PID) control methods and related learning-based policies in terms of accuracy and robustness, particularly in environments with uncertain disturbances. Furthermore, the proposed RL-based control policy exhibits an aggressive response in trajectory tracking, even when the speed of the desired trajectory is increased to 6 m/s. Moreover, the learned policy demonstrates strong vibration suppression capabilities and enables the quadrotor to recover to a hovering state from random initial conditions with shorter response time. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep reinforcement learning

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Automated Parkinson’s Disease Diagnosis Using Decomposition Techniques and Deep Learning for Accurate Gait Analysis

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IEEE Access 2025年 13卷 74078-74091页

作者： Jeba Priya, S. Anand Deva Durai, C. Subathra, M.S.P. Thomas George, S. Jeyabose, Andrew Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore India King Khalid University College of Computer Science Department of Computer Science Abha Saudi Arabia Karunya Institute of Technology and Sciences Department of Biomedical Engineering Tamil Nadu Coimbatore India Karunya Institute of Technology and Sciences Department of Robotics Engineering Tamil Nadu Coimbatore India Manipal Academy of Higher Education Manipal Institute of Technology Department of Computer Science and Engineering Karnataka Manipal India

Parkinson's disease (PD) is a prevalent neurological disorder that significantly impacts posture and gait, leading to movement abnormalities due to malfunctions in the brain and nervous system. Gait signals are essential for identifying PD, and various techniques have been employed for classification, with a focus on spatiotemporal factors. Additionally, cognitive monitoring systems for PD symptoms have been developed. Recent advancements involve decomposing gait signals using techniques such as empirical mode decomposition (EMD), empirical wavelet transform (EWT), and variational mode decomposition (VMD) to streamline data for improved computational efficiency. Machine learning (ML) and deep learning (DL) algorithms are widely used to enhance classification accuracy. This study integrates decomposition techniques with ML algorithms such as support vector machines (SVMs), artificial neural networks (ANNs), decision trees (DTs), and k-nearest neighbors (k-NNs), as well as DL algorithms such as long short-term memory (LSTM), bidirectional long short-term memory (LSTM), and convolutional neural networks (CNNs), for PD classification. The combination of VMD with the 1D-CNN achieved the highest accuracy, sensitivity, and specificity, with values of 99.1 %, 100 %, and 100 %, respectively. This finding suggests a promising approach for further research in this field. The optimized VMD-1D-CNN combination demonstrated significant potential for accurately diagnosing PD based on gait dynamics. The successful application of these methods highlights the importance of advanced signal processing techniques in improving the detection and management of neurological disorders. © 2013 IEEE.

关键词： Wavelet decomposition

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