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Intensive summer course in robotics – Robotcraft

Applied Computing and Informatics 2018年第1-2期16卷 155-179页

作者： Ferreira, N.M. Fonseca Araujo, André Couceiro, M.S. Portugal, David Department of Electrical Engineering (DEE) Engineering Institute of Coimbra (ISEC) Polytechnic of Coimbra (IPC) Rua Pedro Nunes – Quinta da Nora Coimbra Portugal GECAD - Knowledge Research Group on Intelligent Engineering and Computing for Advanced Innovation Development of the Engineering Institute of Porto (ISEP) Polytechnic Institute of Porto (IPP) Porto Portugal INESC TEC – Institute for Systems and Computer Engineering Technology and Science Porto Portugal Ingeniarius Lda Coimbra Portugal

This paper describes a two-month summer intensive course designed to introduce participants with a hands-on technical craft on robotics and to acquire experience in the low-level details of embedded systems. Attendants started this course with a brief introduction to robotics;learned to draw, design and create a personalized 3D structure for their mobile robotic platform and developed skills in embedded systems. They were familiarize with the practices used in robotics, learning to connect all sensors and actuator, developing a typical application on differential kinematic using Arduino, exploring ROS features under Raspberry Pi environment and Arduino – Raspberry Pi communication. Different paradigms and some real applications and programming were addressed on the topic of Artificial Intelligence. Throughout the course, participants were introduced to programming languages (including Python and C++), advanced programming concepts such as ROS, basic API development, system concepts such as I2C and UART serial interfaces, PWM motor control and sensor fusion to improve robotic navigation and localization. This paper describes not just the concept, layout and methodology used on RobotCraft 2017 but also presents the participants knowledge background and their overall opinions, leading to focus on lessons learned and suggestions for future editions. © 2018, N.M. Fonseca Ferreira, André Araujo, M.S. Couceiro and David Portugal.

关键词： International trends in educational robotics Project-based learning ROS

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Microrobots: A Needle‐Type Microrobot for Targeted Drug Delivery by Affixing to a Microtissue (Adv. Healthcare Mater. 7/2020)

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Advanced Healthcare Materials 2020年第7期9卷

作者： Seungmin Lee Jin‐young Kim Junyoung Kim Ali Kafash Hoshiar Jongeon Park Sunkey Lee Jonghyun Kim Salvador Pané Bradley J. Nelson Hongsoo Choi Department of Robotics Engineering DGIST‐ETH Microrobot Research Center Daegu‐Gyeongbuk Institute of Science & Technology (DGIST) 333 Techno jungang‐daero Hyeonpung‐eup Dalseong‐Gun Daegu 42988 Republic of Korea School of Computer Science and Electronic Engineering University of Essex Colchester CO4 3SQ UK School of Mechanical Engineering Sungkyunkwan University 2066 Seobu‐ro Jangan‐gu Suwon‐si Gyeonggi‐do 16419 South Korea Institute of Robotics and Intelligent Systems ETH Zurich Zurich CH‐8092 Switzerland

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Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age

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Nature ecology & evolution 2025年 2025 May 22页

作者： Stephen Pringle Martin Dallimer Mark A Goddard Léni K Le Goff Emma Hart Simon J Langdale Jessica C Fisher Sara-Adela Abad Marc Ancrenaz Fabio Angeoletto Fernando Auat Cheein Gail E Austen Joseph J Bailey Katherine C R Baldock Lindsay F Banin Cristina Banks-Leite Aliyu S Barau Reshu Bashyal Adam J Bates Jake E Bicknell Jon Bielby Petra Bosilj Emma R Bush Simon J Butler Dan Carpenter Christopher F Clements Antoine Cully Kendi F Davies Nicolas J Deere Michael Dodd Rosie Drinkwater Don A Driscoll Guillaume Dutilleux Mads Dyrmann David P Edwards Mohammad S Farhadinia Aisyah Faruk Richard Field Robert J Fletcher Chris W Foster Richard Fox Richard M Francksen Aldina M A Franco Alison M Gainsbury Charlie J Gardner Ioanna Giorgi Richard A Griffiths Salua Hamaza Marc Hanheide Matt W Hayward Marcus Hedblom Thorunn Helgason Sui P Heon Kevin A Hughes Edmund R Hunt Daniel J Ingram George Jackson-Mills Kelly Jowett Timothy H Keitt Laura N Kloepper Stephanie Kramer-Schadt Jim Labisko Frédéric Labrosse Jenna Lawson Nicolas Lecomte Ricardo F de Lima Nick A Littlewood Harry H Marshall Giovanni L Masala Lindsay C Maskell Eleni Matechou Barbara Mazzolai Alistair McConnell Brett A Melbourne Aslan Miriyev Eric Djomo Nana Alessandro Ossola Sarah Papworth Catherine L Parr Ana Payo-Payo Gad Perry Nathalie Pettorelli Rajeev Pillay Simon G Potts Miranda T Prendergast-Miller Lan Qie Persie Rolley-Parnell Stephen J Rossiter Marcus Rowcliffe Heather Rumble Jon P Sadler Christopher J Sandom Asiem Sanyal Franziska Schrodt Sarab S Sethi Adi Shabrani Robert Siddall Simón C Smith Robbert P H Snep Carl D Soulsbury Margaret C Stanley Philip A Stephens P J Stephenson Matthew J Struebig Matthew Studley Martin Svátek Gilbert Tang Nicholas K Taylor Kate D L Umbers Robert J Ward Patrick J C White Mark J Whittingham Serge Wich Christopher D Williams Ibrahim B Yakubu Natalie Yoh Syed A R Zaidi Anna Zmarz Joeri A Zwerts Zoe G Davies Durrell Institute of Conservation and Ecology (DICE) School of Natural Sciences University of Kent Canterbury UK. s.pringle@kent.ac.uk. Centre for Environmental Policy Imperial College London London UK. Department of Geography and Environmental Sciences Northumbria University Newcastle upon Tyne UK. School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK. Synthotech Ltd Milner Court Hornbeam Square Harrogate UK. Durrell Institute of Conservation and Ecology (DICE) School of Natural Sciences University of Kent Canterbury UK. Mechanical Engineering Department University College London London UK. HUTAN SWD Kota Kinabalu Malaysia. Programa de Pós-Graduação em Gestão e Tecnologia Ambiental da Universidade Federal de Rondonópolis Rondonópolis Brasil. Department of Engineering Harper Adams University Newport UK. Applied Ecology Research Group School of Life Sciences Anglia Ruskin University Cambridge UK. Centre for Ecology and Hydrology Penicuik UK. Department of Life Sciences Imperial College London Silwood Park Campus Ascot UK. Department of Urban and Regional Planning Faculty of Earth and Environmental Sciences Bayero University Kano Nigeria. Greenhood Nepal Kathmandu Nepal. Animal Rural & Environmental Sciences Nottingham Trent University Nottinghamshire UK. School of Biological and Environmental Sciences Liverpool John Moores University Liverpool UK. Lincoln Centre for Autonomous Systems University of Lincoln Lincoln UK. Royal Botanic Garden Edinburgh Edinburgh UK. School of Biological Sciences University of East Anglia Norwich Research Park Norwich UK. Digital Ecology Limited Bristol UK. School of Biological Sciences University of Bristol Bristol UK. Department of Computing Imperial College London London UK. Department of Ecology and Evolutionary Biology University of Colorado Boulder CO USA. Faculty of Science Technology Engineering and Mathematics (STEM) The Open University Walton Hall Milt

With biodiversity loss escalating globally, a step change is needed in our capacity to accurately monitor species populations across ecosystems. Robotic and autonomous systems (RAS) offer technological solutions that may substantially advance terrestrial biodiversity monitoring, but this potential is yet to be considered systematically. We used a modified Delphi technique to synthesize knowledge from 98 biodiversity experts and 31 RAS experts, who identified the major methodological barriers that currently hinder monitoring, and explored the opportunities and challenges that RAS offer in overcoming these barriers. Biodiversity experts identified four barrier categories: site access, species and individual identification, data handling and storage, and power and network availability. robotics experts highlighted technologies that could overcome these barriers and identified the developments needed to facilitate RAS-based autonomous biodiversity monitoring. Some existing RAS could be optimized relatively easily to survey species but would require development to be suitable for monitoring of more 'difficult' taxa and robust enough to work under uncontrolled conditions within ecosystems. Other nascent technologies (for instance, new sensors and biodegradable robots) need accelerated research. Overall, it was felt that RAS could lead to major progress in monitoring of terrestrial biodiversity by supplementing rather than supplanting existing methods. Transdisciplinarity needs to be fostered between biodiversity and RAS experts so that future ideas and technologies can be codeveloped effectively.

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Handling Scan-time Parameters in Haptic Surface Classification 7

Handling Scan-time Parameters in Haptic Surface Classificati...

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7th IeeE World Haptics Conference, WHC 2017

作者： Burka, Alex Kuchenbecker, Katherine J. Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States Departments of Mechanical Engineering and Applied Mechanics Computer and Information Science Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States Haptic Intelligence Department Max Planck Institute for Intelligent Systems Stuttgart70569 Germany

ISBN: (纸本)9781509014255

The physical sensations of touch, as measured by the force and vibration felt during contact, strongly depend on the normal force exerted by the end-effector as well as its speed relative to the surface, factors we call 'scan-time parameters.' When researchers record surface interactions for machine learning tasks such as haptic surface recognition, they must either (1) precisely control these parameters, (2) record them alongside the rest of the data, or (3) develop postprocessing that makes surface percepts invariant to scan-time parameters. Here we use multi-class support vector machines to compare the second approach ('scan-dependent features' developed in our prior work) with the third approach ('scanfree features' developed by Strese et al.). We first verify our implementation of the scan-free features by testing on Strese et al.'s published dataset of 69 surfaces, achieving 87.4% crossvalidation accuracy. We then compare the two approaches on new surface interaction data gathered from 28 surfaces with our instrument (the Proton Pack), obtaining 57.9% accuracy using scan-dependent features and 93.3% accuracy using scan-free features, demonstrating superiority of the scan-free features for this task. We further calculate the correlation of all features to scan-time parameters, confirming that the scan-free features are relatively invariant to scan-time parameters. © 2017 IeeE.

关键词： Support vector machines

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Real-time force control of an SEA-based body weight support unit with the 2-DOF control structure

arXiv

引用

arXiv 2018年

作者： Sun, Yubo Lei, Yuqi Zou, Wulin Li, Jianmin Yu, Ningbo Institute of Robotics and Automatic Information Systems Nankai University Haihe Education Park Tianjin300353 China Tianjin Key Laboratory of Intelligent Robotics Nankai University Haihe Education Park Tianjin300353 China Department of Electrical and Computer Engineering Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States School of Mechanical Engineering Tianjin University Tianjin300072 China

Body weight support (BWS) is a fundamental technique in rehabilitation. Along with the dramatic progressing of rehabilitation science and engineering, BWS is quickly evolving with new initiatives and has attracted deep research effort in recent years. We have built up a novel gravity offloading system, in which the patient is allowed to move freely in the three-dimensional Cartesian space and receives support against gravity. Thus, the patients, especially for those that suffer from neurological injury such as stroke or spinal cord injury, can focus their residual motor control capabilities on essential therapeutic trainings of balance and gait. The realtime force control performance is critical for the BWS unit to provide suitable support and avoid disturbance. In this work, we have re-designed our BWS unit with a series elastic actuation structure to improve the human-robot interaction performance. Further, the 2 degrees of freedom (2-DOF) control approach was taken for accurate and robust BWS force control. Both simulation and experimental results have validated the efficacy of the BWS design and real-time control methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Force control

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Virtual vs. real: Trading off simulations and physical experiments in reinforcement learning with Bayesian optimization

Virtual vs. real: Trading off simulations and physical exper...

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2017 IeeE International Conference on robotics and Automation, ICRA 2017

作者： Marco, Alonso Berkenkamp, Felix Hennig, Philipp Schoellig, Angela P. Krause, Andreas Schaal, Stefan Trimpe, Sebastian Max Planck Institute for Intelligent Systems Tübingen Germany Department of Computer Science ETH Zurich Switzerland Canada Computational Learning and Motor Control Lab University of Southern California United States Max Planck ETH Center for Learning Systems Tubingen Germany Zurich Switzerland

ISBN: (纸本)9781509046331

In practice, the parameters of control policies are often tuned manually. This is time-consuming and frustrating. Reinforcement learning is a promising alternative that aims to automate this process, yet often requires too many experiments to be practical. In this paper, we propose a solution to this problem by exploiting prior knowledge from simulations, which are readily available for most robotic platforms. Specifically, we extend Entropy Search, a Bayesian optimization algorithm that maximizes information gain from each experiment, to the case of multiple information sources. The result is a principled way to automatically combine cheap, but inaccurate information from simulations with expensive and accurate physical experiments in a cost-effective manner. We apply the resulting method to a cart-pole system, which confirms that the algorithm can find good control policies with fewer experiments than standard Bayesian optimization on the physical system only. © 2017 IeeE.

关键词： Reinforcement learning

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Reports on the 2017 AAAI spring symposium series

Reports on the 2017 AAAI spring symposium series

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作者： Bohg, Jeannette Boix, Xavier Chang, Nancy Chu, Vivian Churchill, Elizabeth F. Fang, Fei Feldman, Jerome González, Avelino J. Kido, Takashi Lawless, William F. Montaña, José L. Ontañón, Santiago Sinapov, Jivko Sofge, Don Steels, Luc Steenson, Molly Wright Takadama, Keiki Yadav, Amulya Max Planck Institute for Intelligent Systems United States Massachusetts Institute of Technology United States Institute of Robotics and Intelligent Machines Georgia Institute of Technology United States Google United States Harvard University United States University of California Berkeley United States Department of Computer Science University of Central Florida United States Preferred Networks in Japan Japan Paine College in Augusta GA United States Department of Mathematics Statistics and Computer Science University of Cantabria Spain Department of Computer Science Drexel University United States Department of Computer Science University of Texas Austin United States Naval Research Laboratory United States Barcelona Spain Carnegie Mellon University School of Design United States University of Electro-Communications Japan Computer Science Department University of Southern California United States

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's department of computer science, presented the 2017 Spring Symposium Series, held Monday through Wednesday, March 27-29, 2017, on the campus of Stanford University. The eight symposia held were Artificial Intelligence for the Social Good (SS-17-01);Computational Construction Grammar and Natural Language Understanding (SS-17-02);Computational Context: Why It's Important, What It Means, and Can It Be Computed? (SS-17-03);Designing the User Experience of Ma chine-Learning systems (SS-17-04);Interactive Multisensory Object Perception for Embodied Agents (SS-17-05);Learning from Ob servation of Humans (SS-17-06);science of Intelligence: Com putational Principles of Natural and Artificial Intelligence (SS-17-07);and Well-Being AI: From Machine Learning to Subjectivity-Oriented Computing (SS-17-08). This report, compiled from organizers of the symposia, summarizes the research that took place. © Copyright 2017, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Learning systems

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A multimodal and multilevel system for robotics treatment of autism in children

A multimodal and multilevel system for robotics treatment of...

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2016 International Workshop on Social Learning and Multimodal Interaction for Designing Artificial Agents, DAA 2016

作者： Palestra, Giuseppe Varni, Giovanna Chetouani, Mohamed Esposito, Floriana Department of Computer Science University of Bari Bari Italy Institute for Intelligent Systems and Robotics University Pierre and Marie Curie Paris France

ISBN: (纸本)9781450345606

Several studies suggest that robots can play a relevant role to address Autistic Spectrum Disorder (ASD). This paper presents a humanoid social robot-assisted behavioral system based on a therapeutic multilevel treatment protocol customized to improve eye contact, joint attention, symbolic play, and basic emotion recognition. In the system, the robot acts as a social mediator, trying to elicit specific behaviors in child, taking into account his/her multimodal signals. Statistical differences in eye contact and facial expression imitation behaviors after the use of the system are reported as preliminary results.

关键词： Diseases

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Gene expression imputation across multiple brain regions provides insights into schizophrenia risk (vol 51, pg 659, 2019)

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NATURE GENETICS 2019年第6期51卷 1068-1068页

作者： Huckins, Laura M. Dobbyn, Amanda Ruderfer, Douglas M. Hoffman, Gabriel Wang, Weiqing Pardinas, Antonio F. Rajagopal, Veera M. Als, Thomas D. Nguyen, Hoang T. Girdhar, Kiran Boocock, James Roussos, Panos Fromer, Menachem Kramer, Robin Domenici, Enrico Gamazon, Eric R. Purcell, Shaun Demontis, Ditte Borglum, Anders D. Walters, James T. R. O'Donovan, Michael C. Sullivan, Patrick Owen, Michael J. Devlin, Bernie Sieberts, Solveig K. Cox, Nancy J. Im, Hae Kyung Sklar, Pamela Stahl, Eli A. Pamela Sklar Division of Psychiatric Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics and Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Psychiatry Icahn School of Medicine at Mount Sinai New York NY USA Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai New York NY USA Vanderbilt University Medical Center Nashville TN USA MRC Centre for Neuropsychiatric Genetics and Genomics Cardiff University Cardiff UK Department of Biomedicine Aarhus University Aarhus Denmark The Lundbeck Foundation Initiative for Integrative Psychiatric Research iPSYCH Denmark Center for Integrative Sequencing Aarhus University Aarhus Denmark Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA Human Brain Collection Core National Institute of Mental Health Bethesda MD USA Laboratory of Neurogenomic Biomarkers Centre for Integrative Biology (CIBIO) University of Trento Trento Italy Clare Hall University of Cambridge Cambridge UK University of North Carolina at Chapel Hill Chapel Hill NC USA Karolinska Institutet Stockholm Sweden Department of Psychiatry University of Pittsburgh Pittsburgh PA USA Systems Biology Sage Bionetworks Seattle WA USA Section of Genetic Medicine Department of Medicine University of Chicago Chicago IL USA Integrated Technology Research Laboratories Pharmaceutical Research Division Takeda Pharmaceutical Company Limited Fujisawa Japan Neuropsychiatry Section Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Neuropsychiatric Signaling Program Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Psychiatry JJ Peters Virginia Medical Center Bronx NY USA Department of Neuroscience Icahn School of Medicine at Mount Sinai New York New York NY USA Friedman Brain Institute

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.

关键词： Gene expression Genome-wide association studies Schizophrenia Transcriptomics

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Fingerprints selection for topological localization

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AIP Conference Proceedings 2017年第1期1863卷

作者： Vladimir Popov Ural Federal University Department of Intelligent Systems and Robotics Mathematics and Computer Science Institute Lenin st. 51 620083 Ekaterinburg Russian Federation

Problems of visual navigation are extensively studied in contemporary robotics. In particular, we can mention different problems of visual landmarks selection, the problem of selection of a minimal set of visual landmarks, selection of partially distinguishable guards, the problem of placement of visual landmarks. In this paper, we consider one-dimensional color panoramas. Such panoramas can be used for creating fingerprints. Fingerprints give us unique identifiers for visually distinct locations by recovering statistically significant features. Fingerprints can be used as visual landmarks for the solution of various problems of mobile robot navigation. In this paper, we consider a method for automatic generation of fingerprints. In particular, we consider the bounded Post correspondence problem and applications of the problem to consensus fingerprints and topological localization. We propose an efficient approach to solve the bounded Post correspondence problem. In particular, we use an explicit reduction from the decision version of the problem to the satisfiability problem. We present the results of computational experiments for different satisfiability algorithms. In robotic experiments, we consider the average accuracy of reaching of the target point for different lengths of routes and types of fingerprints.

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