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International Multidisciplinary Conference on Computer and Energy Science (SpliTech)

作者： Christopher Iliffe Sprague Víctor de la Asunción-Nadal Alberto García-Fernández Department of Robotics Perception and Learning KTH - Royal Institute of Technology Stockholm Sweden Science for Life Laboratory Stockholm University Solna Sweden Department of Chemistry Division of Applied Physical Chemistry KTH - Royal Institute of Technology Stockholm Sweden Department of Physics and Astronomy Condensed Matter Physics of Energy Materials Division of X-ray Photon Science Uppsala Sweden

ISBN: (数字)9789532901351

ISBN: (纸本)9798350390797

The electrification of society is an essential component of the effort to achieve a fossil-free world, where solar cells will play a central role in the future energy system. The advancement of photovoltaics should be aligned with principles of the circular economy. In the last years, lead halide perovskites have risen as a leading candidate for third-generation solar cells, experiencing rapid advancement. However, the manufacturing of commercial products inevitably generates significant waste and end-of-life devices, leading to potentially severe environmental repercussions. To tackle this challenge, proactive research and development of recycling and recovery technologies for perovskite solar cells are very necessary. Here we introduce a proof of concept, which, to the best of our knowledge, is the first AI-guided method designed to predict the optimal recycling treatment for perovskite solar cells and the first construction of a perovskite recycling dataset. Using sentiment analysis language processing for the first time, we achieved up to 70% accuracy in predicting the correct action for a given device structure. This innovative approach opens up new possibilities for applying sentiment analysis as a tool for e-waste recycling.

关键词： Waste reduction Sentiment analysis Technological innovation Protocols Accuracy Photovoltaic cells Perovskites

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Hearing it Out: Guiding Robot Sound Design through Design Thinking

Hearing it Out: Guiding Robot Sound Design through Design Th...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Brian J. Zhang Bastian Orthmann Ilaria Torre Roberto Bresin Jason Fick Iolanda Leite Naomi T. Fitter Collaborative Robotics and Intelligent Systems Institute Oregon State University Corvallis Oregon USA Division of Robotics Perception & Learning KTH Royal Institute of Technology Stockholm Sweden Division of Media Technology & Interaction Design KTH Royal Institute of Technology Stockholm Sweden Music Department Oregon State University Oregon State University Corvallis Oregon USA

Sound can benefit human-robot interaction, but little work has explored questions on the design of nonverbal sound for robots. The unique confluence of sound design and robotics expertise complicates these questions, as most roboticists do not have sound design expertise, necessitating collaborations with sound designers. We sought to understand how roboticists and sound designers approach the problem of robot sound design through two qualitative studies. The first study followed discussions by robotics researchers in focus groups, where these experts described motivations to add robot sound for various purposes. The second study guided music technology students through a generative activity for robot sound design; these sound designers in-training demonstrated high variability in design intent, processes, and inspiration. To unify the two perspectives, we structured recommendations through the design thinking framework, a popular design process. The insights provided in this work may aid roboticists in implementing helpful sounds in their robots, encourage sound designers to enter into collaborations on robot sound, and give key tips and warnings to both.

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Bots of a Feather: Exploring User perceptions of Group Cohesiveness for Application in Robotic Swarms

Bots of a Feather: Exploring User Perceptions of Group Cohes...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Rebecca Stower Elisabetta Zibetti David St-Onge Division of Robotics Perception and Learning KTH Stockholm Sweden Department of Psychology CHArt-LUTIN Laboratory Paris 8 University Paris France Department of Mechanical Engineering Ecole de Technologie Supérieure Montreal Canada

ISBN: (数字)9781728188591

ISBN: (纸本)9781665406802

Behaviours of robot swarms often take inspiration from biological models, such as ant colonies and bee hives. Yet, understanding how these behaviours are actually perceived by human users has so far received limited attention. In this paper, we use animations to represent different kinds of possible swarm motions intended to communicate specific messages to a human. We explore how these animations relate to the perceived group cohesiveness of the swarm, comprised of five different parameters: synchronising, grouping, following, reacting, and shape forming. We conducted an online user study where 98 participants viewed nine animations of a swarm displaying different behaviours and rated them for perceived group cohesiveness. We found that the parameters of group cohesiveness correlated with the messages the swarm was perceived as communicating. In particular, the message of initiating communication was highly positively correlated with all group parameters, whereas broken communication was negatively correlated. In addition, the importance of specific group parameters differed within each animation. For example, the parameter of grouping was most associated with animations signalling an intervention is needed. These findings are discussed within the context of designing intuitive behaviour for robot swarms.

关键词： Feathers Correlation Shape Biological system modeling Animation Chatbots Robots

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Arbitrarily Applicable Same/Opposite Relational Responding with NARS

arXiv

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arXiv 2025年

作者： Johansson, Robert Hammer, Patrick Lofthouse, Tony Department of Psychology Stockholm University Stockholm Sweden Division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden

Same/opposite relational responding, a fundamental aspect of human symbolic cognition, allows the flexible generalization of stimulus relationships based on minimal experience. In this study, we demonstrate the emergence of arbitrarily applicable same/opposite relational responding within the Non-Axiomatic Reasoning System (NARS), a computational cognitive architecture designed for adaptive reasoning under uncertainty. Specifically, we extend NARS with an implementation of acquired relations, enabling the system to explicitly derive both symmetric (mutual entailment) and novel relational combinations (combinatorial entailment) from minimal explicit training in a contextually controlled matching-to-sample (MTS) procedure. Experimental results show that NARS rapidly internalizes explicitly trained relational rules and robustly demonstrates derived relational generalizations based on arbitrary contextual cues. Importantly, derived relational responding in critical test phases inherently combines both mutual and combinatorial entailments, such as deriving same-relations from multiple explicitly trained opposite-relations. Internal confidence metrics illustrate strong internalization of these relational principles, closely paralleling phenomena observed in human relational learning experiments. Our findings underscore the potential for integrating nuanced relational learning mechanisms inspired by learning psychology into artificial general intelligence frameworks, explicitly highlighting the arbitrary and context-sensitive relational capabilities modeled within NARS. © 2025, CC BY-NC-ND.

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

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

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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

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

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

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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

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LoGS: Visual Localization via Gaussian Splatting with Fewer Training Images

arXiv

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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

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Amortized Variational Inference for Road Friction Estimation 31

Amortized Variational Inference for Road Friction Estimation

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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

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

arXiv

引用

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

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