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

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

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

Ice-Breakers, Turn-Takers and Fun-Makers: Exploring Robots for Groups with Teenagers

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Ice-Breakers, Turn-Takers and Fun-Makers: Exploring Robots f...

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Sarah Gillet Katie Winkle Giulia Belgiovine Iolanda Leite Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden DIBRIS Department CONTACT Unit at Istituto Italiano di Tecnologia Università degli Studi di Genova

ISBN: (数字)9781728188591

ISBN: (纸本)9781665406802

Successful, enjoyable group interactions are important in public and personal contexts, especially for teenagers whose peer groups are important for self-identity and self-esteem. Social robots seemingly have the potential to positively shape group interactions, but it seems difficult to effect such impact by designing robot behaviors solely based on related (human interaction) literature. In this article, we take a user-centered approach to explore how teenagers envisage a social robot "group assistant". We engaged 16 teenagers in focus groups, interviews, and robot testing to capture their views and reflections about robots for groups. Over the course of a two-week summer school, participants co-designed the action space for such a robot and experienced working with/wizarding it for 10+ hours. This experience further altered and deepened their insights into using robots as group assistants. We report results regarding teenagers’ views on the applicability and use of a robot group assistant, how these expectations evolved throughout the study, and their repeat interactions with the robot. Our results indicate that each group moves on a spectrum of need for the robot, reflected in use of the robot more (or less) for ice-breaking, turn-taking, and fun-making as the situation demanded.

关键词： Shape Social robots Training data Reflection Behavioral sciences Interviews Robots

Does what users say match what they do? Comparing self-reported attitudes and behaviours towards a social robot

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Does what users say match what they do? Comparing self-repor...

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Rebecca Stower Karen Tatarian Damien Rudaz Marine Chamoux Mohamed Chetouani Arvid Kappas Division of Robotics Perception and Learning KTH Stockholm Sweden SoftBank Robotics Europe Paris France SoftBank Robotics Europe and l’Institut Polytechnique de Paris Paris France Institut Systèmes Intelligents et de Robotique CNRS 7222 Sorbonne Université Paris France Department of Psychology and Methods Jacobs University Bremen Germany

ISBN: (数字)9781728188591

ISBN: (纸本)9781665406802

Constructs intended to capture social attitudes and behaviour towards social robots are incredibly varied, with little overlap or consistency in how they may be related. In this study we conduct exploratory analyses between participants’ self-reported attitudes and behaviour towards a social robot. We designed an autonomous interaction where 102 participants interacted with a social robot (Pepper) in a hypothetical travel planning scenario, during which the robot displayed various multi-modal social behaviours. Several behavioural measures were embedded throughout the interaction, followed by a self-report questionnaire targeting participant’s social attitudes towards the robot (social trust, liking, rapport, competency trust, technology acceptance, mind perception, social presence, and social information processing). Several relationships were identified between participant’s behaviour and self-reported attitudes towards the robot. Implications for how to conceptualise and measure interactions with social robots are discussed.

关键词： Atmospheric measurements Social robots Information processing Particle measurements Planning

LoGS: Visual Localization via Gaussian Splatting with Fewer Training Images

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arXiv 2024年

作者： Cheng, Yuzhou Jiao, Jianhao Wang, Yue Kanoulas, Dimitrios Robot Perception and Learning Lab Intelligent Robotics Department of Computer Science University College London Gower Street LondonWC1E 6BT United Kingdom Zhejiang University Zhejiang Hangzhou China AI Centre Department of Computer Science University College London Gower Street LondonWC1E 6BT United Kingdom Archimedes/Athena RC Greece

Visual localization involves estimating a query image’s 6-DoF (degrees of freedom) camera pose, which is a fundamental component in various computer vision and robotic tasks. This paper presents LoGS, a vision-based localization pipeline utilizing the 3D Gaussian Splatting (GS) technique as scene representation. This novel representation allows high-quality novel view synthesis. During the mapping phase, structure-from-motion (SfM) is applied first, followed by the generation of a GS map. During localization, the initial position is obtained through image retrieval, local feature matching coupled with a PnP solver, and then a high-precision pose is achieved through the analysis-by-synthesis manner on the GS map. Experimental results on four large-scale datasets demonstrate the proposed approach’s SoTA accuracy in estimating camera poses and robustness under challenging few-shot conditions. © 2024, CC BY.

关键词： Gaussian distribution

Amortized Variational Inference for Road Friction Estimation 31

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Amortized Variational Inference for Road Friction Estimation

31st IEEE Intelligent Vehicles Symposium, IV 2020

作者： Chen, Shuangshuang Ding, Sihao Muppirisetty, L. Srikar Karayiannidis, Yiannis Bjorkman, Marten Division of Robotics Perception Learning Royal Institute of Technology Stockholm100 44 Sweden Volvo Car Corporation Department of Electrical Engineering Chalmers University of Technology Gothenburg412 96 Sweden

Road friction estimation concerns inference of the coefficient between the tire and road surface to facilitate active safety features. Current state-of-the-art methods lack generalization capability to cope with different tire characteristics and models are restricted when using Bayesian inference in estimation while recent supervised learning methods lack uncertainty prediction on estimates. This paper introduces variational inference to approximate intractable posterior of friction estimates and learns an amortized variational inference model from tire measurement data to facilitate probabilistic estimation while sustaining the flexibility of tire models. As a by-product, a probabilistic tire model can be learned jointly with friction estimator model. Experiments on simulated and field test data show that the learned friction estimator provides accurate estimates with robust uncertainty measures in a wide range of tire excitation levels. Meanwhile, the learned tire model reflects well-studied tire characteristics from field test data. © 2020 IEEE.

关键词： Tires

Asymptotically exact and fast Gaussian copula models for imputation of mixed data types

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arXiv 2021年

作者： Christoffersen, Benjamin Clements, Mark Humphreys, Keith Kjellström, Hedvig Department of Medical Epidemiology and Biostatistics Karolinska Institutet Sweden Division of Robotics Perception and Learning KTH Royal Institute of Technology Sweden Swedish e-Science Research Center Sweden

Missing values with mixed data types is a common problem in a large number of machine learning applications such as processing of surveys and in different medical applications. Recently, Gaussian copula models have been suggested as a means of performing imputation of missing values using a probabilistic framework. While the present Gaussian copula models have shown to yield state of the art performance, they have two limitations: they are based on an approximation that is fast but may be imprecise and they do not support unordered multinomial variables. We address the first limitation using direct and arbitrarily precise approximations both for model estimation and imputation by using randomized quasi-Monte Carlo procedures. The method we provide has lower errors for the estimated model parameters and the imputed values, compared to previously proposed methods. We also extend the previous Gaussian copula models to include unordered multinomial variables in addition to the present support of ordinal, binary, and continuous variables. Copyright © 2021, The Authors. All rights reserved.

关键词： Medical applications

LiteVLoc: Map-Lite Visual Localization for Image Goal Navigation

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arXiv 2024年

作者： Jiao, Jianhao He, Jinhao Liu, Changkun Aegidius, Sebastian Hu, Xiangcheng Braud, Tristan Kanoulas, Dimitrios The Robot Perception and Learning Lab Intelligent Robotics Department of Computer Science University College London Gower Street LondonWC1E 6BT United Kingdom Nansha District Guangzhou China The Department of Computer Science and Engineering HKUST Hong Kong The Department of Electronic an Computer Engineering HKUST Hong Kong The AI Centre Department of Computer Science University College London Gower Street LondonWC1E 6BT United Kingdom Archimedes/Athena RC Greece

This paper presents LiteVLoc, a hierarchical visual localization framework that uses a lightweight topo-metric map to represent the environment. The method consists of three sequential modules that estimate camera poses in a coarse-to-fine manner. Unlike mainstream approaches relying on detailed 3D representations, LiteVLoc reduces storage overhead by leveraging learning-based feature matching and geometric solvers for metric pose estimation. A novel dataset for the map-free relocalization task is also introduced. Extensive experiments including localization and navigation in both simulated and real-world scenarios have validate the system's performance and demonstrated its precision and efficiency for large-scale deployment. Code and data will be made publicly available at https://***/LiteVLoc. Copyright © 2024, The Authors. All rights reserved.

关键词： Visualization

Ice-Breakers, Turn-Takers and Fun-Makers: Exploring Robots for Groups with Teenagers

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arXiv 2025年

作者： Gillet, Sarah Winkle, Katie Belgiovine, Giulia Leite, Iolanda Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden CONTACT unit Istituto Italiano di Tecnologia DIBRIS department Università degli Studi di Genova Italy

关键词： Social robots

Interpreting video features: A comparison of 3D convolutional networks and convolutional lstm networks

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arXiv 2020年

作者： Mänttäri, Joonatan Broomé, Sofia Folkesson, John Kjellström, Hedvig Division of Robotics Perception and Learning Department of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

A number of techniques for interpretability have been presented for deep learning in computer vision, typically with the goal of understanding what the networks have actually learned underneath a given classification decision. However, interpretability for deep video architectures is still in its infancy and we do not yet have a clear concept of how to decode spatiotemporal features. In this paper, we present a study comparing how 3D convolutional networks and convolutional LSTM networks learn features across temporally dependent frames. This is the first comparison of two video models that both convolve to learn spatial features but that have principally different methods of modeling time. Additionally, we extend the concept of meaningful perturbation introduced by Fong & Vedaldi (2017) to the temporal dimension to search for the most meaningful part of a sequence for a classification decision. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution