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RL + Model-Based Control: Using On-Demand Optimal Control to Learn Versatile Legged Locomotion

IEEE robotics and Automation Letters 2023年第10期8卷 6619-6626页

作者： Kang, Dongho Cheng, Jin Zamora, Miguel Zargarbashi, Fatemeh Coros, Stelian ETH Zurich Computational Robotics Lab in the Department of Computer Science Zurich8092 Switzerland

This letter presents a control framework that combines model-based optimal control and reinforcement learning (RL) to achieve versatile and robust legged locomotion. Our approach enhances the RL training process by incorporating on-demand reference motions generated through finite-horizon optimal control, covering a broad range of velocities and gaits. These reference motions serve as targets for the RL policy to imitate, leading to the development of robust control policies that can be learned with reliability. Furthermore, by utilizing realistic simulation data that captures whole-body dynamics, RL effectively overcomes the inherent limitations in reference motions imposed by modeling simplifications. We validate the robustness and controllability of the RL training process within our framework through a series of experiments. In these experiments, our method showcases its capability to generalize reference motions and effectively handle more complex locomotion tasks that may pose challenges for the simplified model, thanks to RL's flexibility. Additionally, our framework effortlessly supports the training of control policies for robots with diverse dimensions, eliminating the necessity for robot-specific adjustments in the reward function and hyperparameters. © 2016 IEEE.

关键词： Reinforcement learning

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A Novel Assistive Imaging Device for Colon Map Reconstruction

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IEEE robotics and Automation Letters 2024年第12期9卷 10780-10787页

作者： Dei, Neri Niccolo Bano, Sophia Vasconcelos, Francisco Consumi, Vanni Stoyanov, Danail Stilli, Agostino Ciuti, Gastone Scuola Superiore sant'Anna Bio Robotics Institute Department of Excellence in Robotics and AI Pisa Italy University College London Hawkes Institute Department of Computer Science London United Kingdom

Colonoscopy, recognised as the gold standard for diagnosing and treating colorectal cancer, faces limitations that may result in overlooking some colonic regions. This can lead to missed lesions and interval cancer, leading to incomplete treatment. Addressing this challenge, the authors present a novel Assistive Imaging Device for Colon Map Reconstruction (AID-CMR) to support clinicians in visualising the entire colonic mucosa and identifying areas that may have been missed during the procedure. The device is comprised of an overtube tooled with an imaging module on its distal end, which can be moved about a threaded silicone sleeve. The imaging module houses inflatable silicone chambers to distend the lumen, a side-viewing miniaturised full-HD camera with LEDs, a magnetic encoder, and an irrigation channel for camera cleansing. Before intervention, the device can be installed on standard colonoscopes or other tethered endoscopes. During the intervention, the device can be operated to (1) reconstruct assistive maps of the organ, (2) visualise the full anatomy, and (3) increase disease detection. Moreover, these maps may be employed for the quantification of missed regions during standard inspection. This paper introduces a proof of concept device and discusses locomotion and reconstruction tests conducted in two in-vitro conditions. These tests reveal overall procedural feasibility in terms of locomotion, exerted forces and colon map reconstruction, preserving polyp morphology. © 2024 IEEE.

关键词： Medical robotics

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Medical Recommendation System Based on Collaborative Filtering Algorithms

Medical Recommendation System Based on Collaborative Filteri...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Bogdanova, Diana Shakhmametova, Gyuzel Niiazgulov, Albert Ufa University of Science and Technology Department of Computer Science and Robotics Ufa Russia

ISBN: (纸本)9798350395044

The article deals with the issues of designing a medical recommendation system based on the collaborative filtering algorithm. The problem under consideration is a large number of medical errors, which are one of the most common causes of patient deaths. In Russia alone, more than 70 thousand people suffer severe complications every year for this reason. The aim of the research is to analyze the possibility of designing and implementing software that reduces the probability of occurrence of medical errors and allows to realize the automation of prescribing by a doctor of the optimal treatment for a particular patient. The article analyzes the subject area and problem situation, identifies the causes of a large number of medical errors in public medical institutions in Russia. The problem statement is considered, mathematical support of the recommendation system based on the algorithm of collaborative filtering is proposed. The analysis of methods for solving the problem is given. The implementation of the recommendation system in the Python programming language is considered, the results of research in the form of a computational experiment are analyzed and interpreted. According to the results of the research, it is concluded that the proposed implementation of the medical recommendation system based on the collaborative filtering algorithm can be used in the further design of a full-fledged system and then used in medical institutions as a part of the system. © 2024 IEEE.

关键词： Collaborative filtering

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Deep Transfer Learning Based Detection and Classification of Citrus Plant Diseases

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computers, Materials & Continua 2023年第7期76卷 895-914页

作者： Shah Faisal Kashif Javed Sara Ali Areej Alasiry Mehrez Marzougui Muhammad Attique Khan Jae-Hyuk Cha Department of Robotics and Artificial Intelligence SMME NUSTIslamabadPakistan College of Computer Science King Khalid UniversityAbha61413Saudi Arabia Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science Hanyang UniversitySeoul04763Korea

Citrus fruit crops are among the world’s most important agricultural products,but pests and diseases impact their cultivation,resulting in yield and quality *** vision and machine learning have been widely used to detect and classify plant diseases over the last decade,allowing for early disease detection and improving agricultural *** paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning(DL)model,which improved accuracy while decreasing computational *** most recent transfer learning-based models were applied to the Citrus Plant Dataset to improve classification *** transfer learning,this study successfully proposed a Convolutional Neural Network(CNN)-based pre-trained model(EfficientNetB3,ResNet50,MobiNetV2,and InceptionV3)for the identification and categorization of citrus plant *** evaluate the architecture’s performance,this study discovered that transferring an EfficientNetb3 model resulted in the highest training,validating,and testing accuracies,which were 99.43%,99.48%,and 99.58%,*** identifying and categorizing citrus plant diseases,the proposed CNN model outperforms other cuttingedge CNN model architectures developed previously in the literature.

关键词： Citrus diseases classification deep learning transfer learning efficientNetB3 mobileNetV2 ResNet50 InceptionV3

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

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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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Incomplete and Asymmetric Information in General Lotto Games

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IEEE Transactions on Automatic Control 2024年第6期70卷 3617-3632页

作者： Paarporn, Keith Chandan, Rahul Alizadeh, Mahnoosh Marden, Jason R. University of Colorado Department of Computer Science Colorado Springs United States Amazon Robotics United States University of California Department of Electrical and Computer Engineering Santa BarbaraCA United States

In this paper, we consider incomplete and asymmetric information formulations of the General Lotto game, which describes two opposing players that strategically allocate limited resources over multiple contests. In particular, we consider scenarios where one of the player's resource budget is common knowledge while the other player's is private. Our main contribution provides complete equilibrium characterizations in the scenario where the private resource budget is drawn from a Bernoulli distribution. We then leverage these characterizations to analyze a resource assignment problem where a commander must decide how to assign resources to sub-colonels that compete against opponents in separate General Lotto games. While optimal deterministic policies have previously been characterized in the literature, we broaden the context by deriving optimal randomized policies, which induce asymmetric information General Lotto games. Leveraging our equilibrium characterizations, we demonstrate that randomization can offer a four-fold improvement in the commander's performance over the optimal deterministic assignments. © 1963-2012 IEEE.

关键词： Budget control

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Pose Estimation of an Autonomous Drilling Excavator

Pose Estimation of an Autonomous Drilling Excavator

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Joint International Conference of the 14th International Conference on Mechanisms and Mechanical Transmissions and 26th International Conference on robotics, MTM and robotics 2024

作者： Suiker, Marcel Husemann, Jörg Berns, Karsten Robotics Research Lab Department of Computer Science University of Kaiserslautern-Landau Kaiserslautern Germany

ISBN: (纸本)9783031875366

Due to the increase of automation in the field of construction, more and more automation approaches for excavators have been developed. A special case of excavators are drilling excavators. Therefore, a precise pose estimation is an essential task to achieve automation of the drilling process and has not been the subject of interest in research until now. This paper presents a new approach to calculating a pose of the excavator’s undercarriage for a given drilling position. The positioning uses information on the excavator’s workspace considering its kinematics, the terrain, and obstacles at the construction site. Essential to this approach is the computation of the terrain gradient, as well as the computation of the largest inscribed rectangle inside a computed candidate area. Experiments showed that the presented solution yields good results but takes a long time to find them. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Excavators

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SS3DM: Benchmarking Street-View Surface Reconstruction with a Synthetic 3D Mesh Dataset 38

SS3DM: Benchmarking Street-View Surface Reconstruction with ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Yubin Wen, Kairui Zhou, Heng Guo, Xiaoyang Liu, Yong-Jin Department of Computer Science and Technology Tsinghua University China Horizon Robotics China

Reconstructing accurate 3D surfaces for street-view scenarios is crucial for applications such as digital entertainment and autonomous driving simulation. However, existing street-view datasets, including KITTI, Waymo, and nuScenes, only offer noisy LiDAR points as ground-truth data for geometric evaluation of reconstructed surfaces. These geometric ground-truths often lack the necessary precision to evaluate surface positions and do not provide data for assessing surface normals. To overcome these challenges, we introduce the SS3DM dataset, comprising precise Synthetic Street-view 3D Mesh models exported from the CARLA simulator. These mesh models facilitate accurate position evaluation and include normal vectors for evaluating surface normal. To simulate the input data in realistic driving scenarios for 3D reconstruction, we virtually drive a vehicle equipped with six RGB cameras and five LiDAR sensors in diverse outdoor scenes. Leveraging this dataset, we establish a benchmark for state-of-the-art surface reconstruction methods, providing a comprehensive evaluation of the associated challenges. For more information, visit our homepage at https://***. © 2024 Neural information processing systems foundation. All rights reserved.

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Rapid Assessment of MANET Reliability Trends

Rapid Assessment of MANET Reliability Trends

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2023 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2023

作者： Gvozdev, Vladimir Davlieva, Aliya Galimov, Robert Ufa University of Science and Technology Department of Computer Science and Robotics Ufa Russia

ISBN: (纸本)9781665475952

The topological features of distributed infocommunication networks are among the key factors affecting such information safety characteristics as stability;reliability and resistance to failures and attacks. In the framework of this work, the postulate is postulated that the consequences of the security threats implementation are disconnections between the Wireless Mobile ad hoc network nodes. The metric characteristic of the consequences is a change in the system reliability indicators. The paper substantiates the possibility of using well-known methods for identifying trends to build estimates of the integral security characteristics of the Wireless Mobile ad hoc network;apparatus of order statistics;statistic indexes;linguistic variables. © 2023 IEEE.

关键词： Reliability

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TECMH:Transformer-Based Cross-Modal Hashing For Fine-Grained Image-Text Retrieval

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computers, Materials & Continua 2023年第5期75卷 3713-3728页

作者： Qiqi Li Longfei Ma Zheng Jiang Mingyong Li Bo Jin College of Computer and Information Science Chongqing Normal UniversityChongqing401331China Institute of Systems and Robotics(ISR) Department of Electrical and Computer Engineering(DEEC)University of CoimbraCoimbraPortugal

In recent years,cross-modal hash retrieval has become a popular research field because of its advantages of high efficiency and low ***-modal retrieval technology can be applied to search engines,crossmodalmedical processing,*** existing main method is to use amulti-label matching paradigm to finish the retrieval ***,such methods do not use fine-grained information in the multi-modal data,which may lead to suboptimal *** avoid cross-modal matching turning into label matching,this paper proposes an end-to-end fine-grained cross-modal hash retrieval method,which can focus more on the fine-grained semantic information of multi-modal ***,the method refines the image features and no longer uses multiple labels to represent text features but uses BERT for ***,this method uses the inference capabilities of the transformer encoder to generate global fine-grained ***,in order to better judge the effect of the fine-grained model,this paper uses the datasets in the image text matching field instead of the traditional label-matching *** article experiment on Microsoft COCO(MS-COCO)and Flickr30K datasets and compare it with the previous *** experimental results show that this method can obtain more advanced results in the cross-modal hash retrieval field.

关键词： Deep learning cross-modal retrieval hash learning transformer

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