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Grapevine Winter Pruning Automation: On Potential Pruning Points Detection through 2D Plant Modeling using Grapevine Segmentation 11

Grapevine Winter Pruning Automation: On Potential Pruning Po...

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11th IEEE Annual International Conference on CYBER Technology in Automation, Control, and intelligent Systems, CYBER 2021

作者： Fernandes, Miguel Scaldaferri, Antonello Fiameni, Giuseppe Teng, Tao Gatti, Matteo Poni, Stefano Semini, Claudio Caldwell, Darwin Chen, Fei Active Perception and Robot Interactive Learning Laboratory Istituto Italiano di Tecnologia Department of Advanced Robotics Genova16163 Italy Italy Università Cattolica Del Sacro Cuore Department of Sustainable Crop Production Piacenza29122 Italy Lab Istituto Italiano di Tecnologia Genova16163 Italy T-Stone Robotics Institute The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Hong Kong

ISBN: (纸本)9781665425278

Grapevine winter pruning is a complex task, that requires skilled workers to execute it correctly. The complexity of this task is also the reason why it is time consuming. Considering that this operation takes about 80-120 hours/ha to be completed, and therefore is even more crucial in large-size vineyards, an automated system can help to speed up the process. To this end, this paper presents a novel multidisciplinary approach that tackles this challenging task by performing object segmentation on grapevine images, used to create a representative model of the grapevine plants. Second, a set of potential pruning points is generated from this plant representation. We will describe (a) a methodology for data acquisition and annotation, (b) a neural network fine-tuning for grapevine segmentation, (c) an image processing based method for creating the representative model of grapevines, starting from the inferred segmentation and (d) potential pruning points detection and localization, based on the plant model which is a simplification of the grapevine structure. With this approach, we are able to identify a significant set of potential pruning points on the canes, that can be used, with further selection, to derive the final set of the real pruning points. © 2021 IEEE.

关键词： Data acquisition

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learning Dynamic-Objective Policies from a Class of Optimal Trajectories

Learning Dynamic-Objective Policies from a Class of Optimal ...

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IEEE Conference on Decision and Control

作者： Christopher Iliffe Sprague Dario Izzo Petter Ögren Robotics Perception and Learning Lab. Royal Institute of Technology (KTH) Stockholm Sweden Advanced Concepts Team European Space Technology Center (ESTEC) Noordwijk The Netherlands

ISBN: (数字)9781728174471

ISBN: (纸本)9781728174488

Optimal state-feedback controllers, capable of changing between different objective functions, are advantageous to systems in which unexpected situations may arise. However, synthesising such controllers, even for a single objective, is a demanding process. In this paper, we present a novel and straightforward approach to synthesising these policies through a combination of trajectory optimisation, homotopy continuation, and imitation learning. We use numerical continuation to efficiently generate optimal demonstrations across several objectives and boundary conditions, and use these to train our policies. Additionally, we demonstrate the ability of our policies to effectively learn families of optimal state- feedback controllers, which can be used to change objective functions online. We illustrate this approach across two trajectory optimisation problems, an inverted pendulum swingup and a spacecraft orbit transfer, and show that the synthesised policies, when evaluated in simulation, produce trajectories that are near-optimal. These results indicate the benefit of trajectory optimisation and homotopy continuation to the synthesis of controllers in dynamic-objective contexts.

关键词： Trajectory Optimization Optimal control Linear programming Boundary conditions Robots Process control

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A Data-Driven Method for Estimating Formation Flexibility in Beyond-Visual-Range Air Combat

A Data-Driven Method for Estimating Formation Flexibility in...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Edvards Scukins Andre N. Costa Petter Ögren Aeronautical Solutions Division SAAB Aeronautics Robotics Perception and Learning Lab. Royal Institute of Technology (KTH) Decision Support Systems Subdivision Institute for Advanced Studies (IEAv)

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

Tactical decisions in air combat are typically evaluated using experience as a basis. Pilots undergo frequent training in various air combat processes to enhance their combat proficiency and evaluation skills. Having the Situational Awareness (SA) necessary to evaluate the effects of multiple missile threats can often be challenging. This study provides a new method for calculating an aircraft fleet's maneuver flexibility in a Beyond-Visual-Range (BVR) setting. Sustaining a high degree of flexibility is necessary to adapt to unforeseen circumstances in BVR air combat. To do that, we employ Deep Neural Networks (DNN) to capture the result of a high-performance aircraft model in the presence of adversarial BVR missiles. We then modify our approach to calculate the aircraft's maneuverability concerning an opposing fleet, looking at the advantages and disadvantages of several flight formations. Finally, we consider the anticipated threat from an incoming opponent formation and optimize the counter-formation. This methodology offers a more sophisticated comprehension of aircraft maneuver flexibility within a BVR framework and aids in developing flexible and efficient decision-making techniques for air combat.

关键词： Training Measurement Missiles Visualization Atmospheric modeling Decision making Artificial neural networks

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Visual Object Tracking across Diverse Data Modalities: A Review

arXiv

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arXiv 2024年

作者： Wang, Mengmeng Ma, Teli Xin, Shuo Hou, Xiaojun Xing, Jiazheng Dai, Guang Wang, Jingdong Liu, Yong The Laboratory of Advanced Perception on Robotics and Intelligent Learning College of Control Science and Engineering Zhejiang University Zhejiang Hangzhou310027 China State Grid Shanxi Electric Power Company Limited China Baidu China

Visual Object Tracking (VOT) is an attractive and significant research area in computer vision, which aims to recognize and track specific targets in video sequences where the target objects are arbitrary and class-agnostic. The VOT technology could be applied in various scenarios, processing data of diverse modalities such as RGB, thermal infrared and point cloud. Besides, since no one sensor could handle all the dynamic and varying environments, multi-modal VOT is also investigated. This paper presents a comprehensive survey of the recent progress of both single-modal and multi-modal VOT, especially the deep learning methods. Specifically, we first review three types of mainstream single-modal VOT, including RGB, thermal infrared and point cloud tracking. In particular, we conclude four widely-used single-modal frameworks, abstracting their schemas and categorizing the existing inheritors. Then we summarize four kinds of multi-modal VOT, including RGB-Depth, RGB-Thermal, RGB-LiDAR and RGB-Language. Moreover, the comparison results in plenty of VOT benchmarks of the discussed modalities are presented. Finally, we provide recommendations and insightful observations, inspiring the future development of this fast-growing literature. © 2024, CC BY.

关键词： Data handling

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TryOn-Adapter: Efficient Fine-Grained Clothing Identity Adaptation for High-Fidelity Virtual Try-On

arXiv

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arXiv 2024年

作者： Xing, Jiazheng Xu, Chao Qian, Yijie Liu, Yang Dai, Guang Sun, Baigui Liu, Yong Wang, Jingdong Laboratory of Advanced Perception on Robotics and Intelligent Learning College of Control Science and Engineering Zhejiang University Zhejiang Hangzhou310027 China Alibaba Group China SGIT AI Lab State Grid Shaanxi Electric Power Company China Baidu Inc China

Virtual try-on focuses on adjusting the given clothes to fit a specific person seamlessly while avoiding any distortion of the patterns and textures of the garment. However, the clothing identity uncontrollability and training inefficiency of existing diffusion-based methods, which struggle to maintain the identity even with full parameter training, are significant limitations that hinder the widespread applications. In this work, we propose an effective and efficient framework, termed TryOn-Adapter. Specifically, we first decouple clothing identity into fine-grained factors: style for color and category information, texture for high-frequency details, and structure for smooth spatial adaptive transformation. Our approach utilizes a pre-trained exemplar-based diffusion model as the fundamental network, whose parameters are frozen except for the attention layers. We then customize three lightweight modules (Style Preserving, Texture Highlighting, and Structure Adapting) incorporated with fine-tuning techniques to enable precise and efficient identity control. Meanwhile, we introduce the training-free T-RePaint strategy to further enhance clothing identity preservation while maintaining the realistic try-on effect during the inference. Our experiments demonstrate that our approach achieves state-of-the-art performance on two widely-used benchmarks. Additionally, compared with recent full-tuning diffusion-based methods, we only use about half of their tunable parameters during training. The code will be made publicly available at https://***/jiazhengxing/TryOn-Adapter. © 2024, CC BY-NC-ND.

关键词： Textures

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Control of legged robots with optimal distribution of contact forces

Control of legged robots with optimal distribution of contac...

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2011 11th IEEE-RAS International Conference on Humanoid Robots, HUMANOIDS 2011

作者： Righetti, Ludovic Buchli, Jonas Mistry, Michael Schaal, Stefan Computational Learning and Motor Control Lab. University of Southern California Los Angeles CA 90089 United States Max Planck Institute for Intelligent Systems Tübingen Germany Dept. of Advanced Robotics Italian Institute of Technology Genoa Italy Disney Research Pittsburgh Pittsburgh PA 15213 United States

ISBN: (纸本)9781612848679

The development of agile and safe humanoid robots require controllers that guarantee both high tracking performance and compliance with the environment. More specifically, the control of contact interaction is of crucial importance for robots that will actively interact with their environment. Model-based controllers such as inverse dynamics or operational space control are very appealing as they offer both high tracking performance and compliance. However, while widely used for fully actuated systems such as manipulators, they are not yet standard controllers for legged robots such as humanoids. Indeed such robots are fundamentally different from manipulators as they are underactuated due to their floating-base and subject to switching contact constraints. In this paper we present an inverse dynamics controller for legged robots that use torque redundancy to create an optimal distribution of contact constraints. The resulting controller is able to minimize, given a desired motion, any quadratic cost of the contact constraints at each instant of time. In particular we show how this can be used to minimize tangential forces during locomotion, therefore significantly improving the locomotion of legged robots on difficult terrains. In addition to the theoretical result, we present simulations of a humanoid and a quadruped robot, as well as experiments on a real quadruped robot that demonstrate the advantages of the controller. © 2011 IEEE.

关键词： Controllers

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Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection

arXiv

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arXiv 2022年

作者： Liang, Yufei Zhang, Jiangning Zhao, Shiwei Wu, Runze Liu, Yong Pan, Shuwen The Laboratory of Advanced Perception on Robotics and Intelligent Learning College of Control Science and Enginneering Zhejiang University Hangzhou310027 China The Fuxi AI Lab NetEase Games Hangzhou310012 China The Discipline of Control Science and Engineering School of Information and Electrical Engineering Zhejiang University City College Hangzhou310015 China

Density-based and classification-based methods have ruled unsupervised anomaly detection in recent years, while reconstruction-based methods are rarely mentioned for the poor reconstruction ability and low performance. However, the latter requires no costly extra training samples for the unsupervised training that is more practical, so this paper focuses on improving reconstruction-based method and proposes a novel Omnifrequency Channel-selection Reconstruction (OCR-GAN) network to handle sensory anomaly detection task in a perspective of frequency. Concretely, we propose a Frequency Decoupling (FD) module to decouple the input image into different frequency components and model the reconstruction process as a combination of parallel omni-frequency image restorations, as we observe a significant difference in the frequency distribution of normal and abnormal images. Given the correlation among multiple frequencies, we further propose a Channel Selection (CS) module that performs frequency interaction among different encoders by adaptively selecting different channels. Abundant experiments demonstrate the effectiveness and superiority of our approach over different kinds of methods, e.g., achieving a new state-of-the-art 98.3 detection AUC on the MVTec AD dataset without extra training data that markedly surpasses the reconstruction-based baseline by +38.1↑ and the current SOTA by +0.3↑. Copyright © 2022, The Authors. All rights reserved.

关键词： Anomaly detection

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Probabilistic Spiking Neural Network for Robotic Tactile Continual learning

Probabilistic Spiking Neural Network for Robotic Tactile Con...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Senlin Fang Yiwen Liu Chengliang Liu Jingnan Wang Yuanzhe Su Yupo Zhang Hoiio Kong Zhengkun Yi Xinyu Wu City University of Macau Macau China The Department of Intelligent Systems and Robot Learning Lab ISRL Group SIAT Branch Institute of Artificial Intelligence and Robotics for Society Shenzhen Institute of Advanced Technology Shenzhen

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The sense of touch is essential for robots to perform various daily tasks. Artificial Neural Networks have shown significant promise in advancing robotic tactile learning. However, due to the changing of tactile data distribution as robots encounter new tasks, ANN-based robotic tactile learning suffers from catastrophic forgetting. To solve this problem, we introduce a novel continual learning (CL) framework called the Probabilistic Spiking Neural Network with Variational Continual learning (PSNN-VCL). In this framework, PSNN introduces uncertainty during spike emission and can apply fast Variational Inference by optimizing the uncertainty through backpropagation, which significantly reduces the required model parameters for VCL. We establish a robotic tactile CL benchmark using publicly available datasets to evaluate our method. Experimental results demonstrated that, compared to other CL methods, PSNN-VCL not only achieves superior performance in terms of widely used CL metrics but also achieves at least a 50% reduction in model parameters on the robotic tactile CL benchmark.

关键词： Continuing education Measurement Uncertainty Spiking neural networks learning (artificial intelligence) Benchmark testing Robot sensing systems

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Road detection through CRF based LiDAR-camera fusion

Road detection through CRF based LiDAR-camera fusion

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2019 International Conference on robotics and Automation, ICRA 2019

作者： Gu, Shuo Zhang, Yigong Tang, Jinhui Yang, Jian Kong, Hui PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China Institute of Advanced Artifical Intelligence in Nanjing Horizon Robotics China

ISBN: (纸本)9781538681763

In this paper, we propose a road detection method with LiDAR-camera fusion in a novel conditional random field (CRF) framework to exploit both range and color information. In the LiDAR based part, a fast height-difference based scanning strategy is applied in the 2D LiDAR range-image domain and a dense road detection result in camera image domain can be obtained through geometric upsampling given the LiDAR-camera calibration parameters. In the camera based part, a fully convolutional network is applied in the camera image domain. Finally, we fuse the dense and binary road detection results from both LiDAR and camera in a single CRF framework. Experiments show that using a single thread of CPU, the proposed LiDAR based part can operate at a frequency of over 250Hz with sparse output in range image and 40Hz with dense result in camera image for the 64-beam Velodyne scanner. Our CRF fusion method achieves very promising road detection performance on the KITTI-Road dataset. © 2019 IEEE.

关键词： Laser radar Roads Cameras Three-dimensional displays Two dimensional displays Image segmentation Transforms

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MA-FSAR: Multimodal Adaptation of CLIP for Few-Shot Action Recognition

arXiv

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arXiv 2023年

作者： Xing, Jiazheng Xu, Chao Wang, Mengmeng Dai, Guang Sun, Baigui Liu, Yong Wang, Jingdong Zhao, Jian Laboratory of Advanced Perception on Robotics and Intelligent Learning College of Control Science and Engineering Zhejiang University Zhejiang Hangzhou310027 China Alibaba Group China College of Computer Science and Technology Zhejiang University of Technology China SGIT AI Lab State Grid Corporation of China China Baidu China China Northwestern Polytechnical University Shanxi Xi’an China

Applying large-scale vision-language pre-trained models like CLIP to few-shot action recognition (FSAR) can significantly enhance both performance and efficiency. While several studies have recognized this advantage, most of them resort to full-parameter fine-tuning to make CLIP’s visual encoder adapt to the FSAR data, which not only costs high computations but also overlooks the potential of the visual encoder to engage in temporal modeling and focus on targeted semantics directly. To tackle these issues, we introduce MA-FSAR, a framework that employs the Parameter-Efficient Fine-Tuning (PEFT) technique to enhance the CLIP visual encoder in terms of action-related temporal and semantic representations. Our solution involves a Fine-grained Multimodal Adaptation, which is different from the previous attempts of PEFT in regular action recognition. Specifically, we first insert a Global Temporal Adaptation that only receives the class token to capture global motion cues efficiently. Then these outputs integrate with visual tokens to enhance local temporal dynamics by a Local Multimodal Adaptation, which incorporates text features unique to the FSAR support set branch to highlight fine-grained semantics related to actions. In addition to these token-level designs, we propose a prototype-level text-guided construction module to further enrich the temporal and semantic characteristics of video prototypes. Extensive experiments demonstrate our superior performance in various tasks using minor trainable parameters. © 2023, CC BY-SA.

关键词： Semantics

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