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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Basilio Noris Mandy Barker Jacqueline Nadel Francois Hentsch Francois Ansermet Aude Billard Learning Algorithms and Systems Laboratory Ecole Polytechnique Fdrale de Lausanne (EPFL) Department of Child and Adolescent Psychiatry Centre Hospitalier Universitaire de Lausanne (CHUV) CNRS Emotion Center Universite Pierre et Marie Curie Department of Child and Adolescent Psychiatry (SPEA) School of Medicine University of Geneva

ISBN: (纸本)9781424441211

In this paper, we report on a study on gaze behavior by children with Autism Spectrum Disorder (ASD) during a dyadic interaction in a naturalistic environment. Twelve children with ASD were contrasted to twelve typically developing (TD) children, in a semi-structured interaction with a selection of items from the Early Social Communication Scale (ESCS). We used the WearCam, a novel head-mounted eye-tracker designed for children, to obtain gaze information across the broad field of view from the viewpoint of the child. Children with ASD looked downwards more often, and explored their lateral field of view more extensively compared to TD children. We discuss a number of hypotheses in support of these observations.

关键词： field of view Autism Spectrum Disorders Child Ocular Fixation heart atrium septum defect Infant dyadic interaction Comment

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Open Robotics Research Using Web-based Knowledge Services

Open Robotics Research Using Web-based Knowledge Services

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IEEE International Conference on Robotics and Automation

作者： Michael Beetz Daniel BeBler Jan Winkler Jan-Hendrik Worch Ferenc Bálint-Benczédi Georg Bartels Aude Billard Asil Kaan Bozcuoglu Zhou Fang Nadia Figueroa Andrei Haidu Hagen Langer Alexis Maldonado Ana Lucia Pais Ureche Moritz Tenorth Thiemo Wiedemeyer the Institute for Artificial Intelligence Universit?t Bremen Am Fallturm 1 28359 Bremen Germany. the Learning Algorithms and Systems Laboratory Ecole Polytechnique Fédérale de Lausanne CH-1015 Lausanne Switzerland the Magazino GmbH Landsberger Str. 234 80687 München Germany

ISBN: (纸本)9781467380270

In this paper we discuss how the combination of modern technologies in "big data" storage and management, knowledge representation and processing, cloud-based computation, and web technology can help the robotics community to establish and strengthen an open research discipline. We describe how we made the demonstrator of a EU project review openly available to the research community. Specifically, we recorded episodic memories with rich semantic annotations during a pizza preparation experiment in autonomous robot manipulation. Afterwards, we released them as an open knowledge base using the cloud- and web-based robot knowledge service OPENEASE. We discuss several ways on how this open data can be used to validate our experimental reports and to tackle novel challenging research problems.

关键词： how combination modern

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Transfer learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges

arXiv

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arXiv 2023年

作者： Jaquier, Noémie Welle, Michael C. Gams, Andrej Yao, Kunpeng Fichera, Bernardo Billard, Aude Ude, Aleš Asfour, Tamim Kragic, Danica Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany KTH Royal Institute of Technology Stockholm Sweden Jožef Stefan Institute Ljubljana Slovenia Learning Algorithms and Systems Laboratory Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Faculty of Electrical Engineering University of Ljubljana Slovenia

Transfer learning is a conceptually-enticing paradigm in pursuit of truly intelligent embodied agents. The core concept — reusing prior knowledge to learn in and from novel situations — is successfully leveraged by humans to handle novel situations. In recent years, transfer learning has received renewed interest from the community from different perspectives, including imitation learning, domain adaptation, and transfer of experience from simulation to the real world, among others. In this paper, we unify the concept of transfer learning in robotics and provide the first taxonomy of its kind considering the key concepts of robot, task, and environment. Through a review of the promises and challenges in the field, we identify the need of transferring at different abstraction levels, the need of quantifying the transfer gap and the quality of transfer, as well as the dangers of negative transfer. Via this position paper, we hope to channel the effort of the community towards the most significant roadblocks to realize the full potential of transfer learning in robotics. Copyright © 2023, The Authors. All rights reserved.

关键词： Knowledge management

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Recent Advances in Robot learning from Demonstration

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Control, Robotics, and Autonomous systems 1000年第1期3卷 297-330页

作者： Harish Ravichandar Athanasios S. Polydoros Sonia Chernova Aude Billard 1Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta Georgia 30332 USA email: harish.ravichandar@gatech.educhernova@gatech.edu 2Learning Algorithms and Systems Laboratory École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland email: athanasios.polydoros@epfl.chaude.billard@epfl.ch

In the context of robotics and automation, learning from demonstration (LfD) is the paradigm in which robots acquire new skills by learning to imitate an expert. The choice of LfD over other robot learning methods is compelling when ideal behavior can be neither easily scripted (as is done in traditional robot programming) nor easily defined as an optimization problem, but can be demonstrated. While there have been multiple surveys of this field in the past, there is a need for a new one given the considerable growth in the number of publications in recent years. This review aims to provide an overview of the collection of machine-learning methods used to enable a robot to learn from and imitate a teacher. We focus on recent advancements in the field and present an updated taxonomy and characterization of existing methods. We also discuss mature and emerging application areas for LfD and highlight the significant challenges that remain to be overcome both in theory and in practice.

关键词： learning from demonstration imitation learning programming by demonstration robot learning

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Neurocomputational modeling of imitation deficits

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BMC Neuroscience 2008年第1期9卷 1-1页

作者： Biljana Petreska Aude G Billard Learning Algorithms and Systems Laboratory (LASA) Ecole Polytechnique Fédérale de Lausanne (EPFL) CH-1015 Lausanne Switzerland

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Roombots-mechanical design of self-reconfiguring modular robots for adaptive furniture

Roombots-mechanical design of self-reconfiguring modular rob...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Alexander Sproewitz Aude Billard Pierre Dillenbourg Auke Jan Ijspeert Bio-Inspired Robotics Group (BIRG) École Polytechnique Fédérale de Lausanne Lausanne Switzerland Learning Algorithms and Systems Laboratory (LASA) École Polytechnique Fédérale de Lausanne Lausanne Switzerland Centre de Recherche et d'Appui pour la Formation (CRAFT) École Polytechnique Fédérale de Lausanne Lausanne Switzerland

We aim at merging technologies from information technology, roomware, and robotics in order to design adaptive and intelligent furniture. This paper presents design principles for our modular robots, called Roombots, as future building blocks for furniture that moves and self-reconfigures. The reconfiguration is done using dynamic connection and disconnection of modules and rotations of the degrees of freedom. We are furthermore interested in applying Roombots towards adaptive behaviour, such as online learning of locomotion patterns. To create coordinated and efficient gait patterns, we use a Central Pattern Generator (CPG) approach, which can easily be optimized by any gradient-free optimization algorithm. To provide a hardware framework we present the mechanical design of the Roombots modules and an active connection mechanism based on physical latches. Further we discuss the application of our Roombots modules as pieces of a homogenic or heterogenic mix of building blocks for static structures.

关键词： Robot kinematics Intelligent robots Robotics and automation Hardware Pervasive computing Robotic assembly Merging Information technology Ubiquitous computing Augmented reality

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Graph Signature for Self-Reconfiguration Planning

Graph Signature for Self-Reconfiguration Planning

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2008 IEEE/RSJ International Conference on Intelligent Robots and systems, Pages 526-1042

作者： Masoud Asadpour Alexander Sproewitz Aude Billard Pierre Dillenbourg Auke Jan Ijspeert ECE Dept. University of Tehran Control and Intelligent Processing Center of Excellence Iran Bio-Inspired Robotics Group (BIRG) Switzerland Learning Algorithms and Systems Laboratory (LASA) Switzerland Centre de Recherche et d Appui pour la Formation Switzerland Centre de Recherche et d'Appui pour la Formation (CRAFT) Ecole Polytechnique Fédérale de Lausanne (EPFL) Switzerland Bio-Inspired Robotics Group (BIRG)

ISBN: (纸本)9781424420575

This project incorporates modular robots as building blocks for furniture that moves and self-reconfigures. The reconfiguration is done using dynamic connection/disconnection of modules and rotations of the degrees of freedom. This paper introduces a new approach to self-reconfiguration planning for modular robots based on the graph signature and the graph edit-distance. The method has been tested in simulation on two type of modules: YaMoR and M-TRAN. The simulation results shows interesting features of the approach, namely rapidly finding a near-optimal solution.

关键词： Modular self-reconfigurable robots Adaptive furniture Graph isomorphism Graph signature Graph edit distance Modular robots graph editing Line graph Reconfiguration Furnishings signatures Building blocks Degree of freedom crop rotation

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On-orbit measurements of the ISS atmosphere by the vehicle cabin atmosphere monitor

On-orbit measurements of the ISS atmosphere by the vehicle c...

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41st International Conference on Environmental systems 2011, ICES 2011

作者： Darrach, M.R. Chutjian, A. Bornstein, B.J. Croonquist, A.P. Garkanian, V. Haemmerle, V.R. Hofman, J. Heinrichs, W.M. Karmon, D. Kenny, J. Kidd, R.D. Lee, S. MacAskill, J.A. Madzunkov, S.M. Mandrake, L. Rust, T.M. Schaefer, R.T. Thomas, J.L. Toomarian, N. Jet Propulsion Laboratory California Institute of Technology Pasadena CA 91109 United States Atomic and Molecular Physics Group JPL/Caltech United States Instrument Autonomy Group JPL/Caltech United States MicroDevices Group JPL/CalTech United States Optical Communications Group JPL/Caltech United States Processing Algorithms and Calibration Engineering JPL/Caltech United States Inst System Engineering JPL/Caltech United States Instruments and Science Data Systems JPL/Caltech United States Instrument Integration and Test JPL/Caltech United States Planetary Chemistry and Astrobiology JPL/Caltech United States High Capacity Computing and Modeling JPL/Caltech United States Machine Learning and Instrument Autonomy Group JPL/Caltech United States SpaceX 1 Rocket Rd Hawthorne CA 90250 United States Advanced Computer Systems and Technology JPL/Caltech United States Advanced Instrument Concepts JPL/Caltech United States

ISBN: (纸本)9781600869488

We report on trace gas and major atmospheric constituents results obtained by the Vehicle Cabin Atmosphere Monitor (VCAM) during operations aboard the International Space Station (ISS). VCAM is an autonomous environmental monitor based on a miniature gas chromatograph/mass spectrometer. It was flown to the ISS on shuttle mission STS-131 and commenced operations on 6/10/10. VCAM provides measurements of ppb-to-ppm levels of volatile trace-gas constituents, and of the atmospheric major constituents (nitrogen, oxygen, argon, and carbon dioxide) in a space vehicle or station. It is designed to operate autonomously and maintenance-free, approximately once per day, with a self-contained gas supply sufficient for a one-year lifetime. VCAM is designed to detect and identify 90% of the target compounds at their 180-day Spacecraft Maximum Allowable Concentration levels. © 2011 by the American Institute of Aeronautics and Astronautics, Inc.

关键词： Gases

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iScience 2023年第9期26卷 107524页

作者： Iwane, Fumiaki Sobolewski, Aleksander Chavarriaga, Ricardo Millán, José del R. Department of Electrical and Computer Engineering The University of Texas at Austin Austin 78712 TX United States Department of Neurology The University of Texas at Austin Austin 78712 TX United States Learning Algorithms and Systems Laboratory (LASA) École Polytechnique Fédérale de Lausanne (EPFL) Lausanne 1015 Switzerland Wyss Center for Bio and Neuroengineering Campus Biotech Genève 1202 Switzerland École Polytechnique Fédérale de Lausanne (EPFL) Campus Biotech Genève 1202 Switzerland Centre for Artificial Intelligence Zurich University of Applied Sciences (ZHAW) Winterthur 8401 Switzerland Mulva Clinic for the Neurosciences The University of Texas at Austin Austin 78712 TX United States

Error-related potentials (ErrPs) are a prominent electroencephalogram (EEG) correlate of performance monitoring, and so crucial for learning and adapting our behavior. It is poorly understood whether ErrPs encode further information beyond error awareness. We report an experiment with sixteen participants over three sessions in which occasional visual rotations of varying magnitude occurred during a cursor reaching task. We designed a brain-computer interface (BCI) to detect ErrPs that provided real-time feedback. The individual ErrP-BCI decoders exhibited good transfer across sessions and scalability over the magnitude of errors. A non-linear relationship between the ErrP-BCI output and the magnitude of errors predicts individual perceptual thresholds to detect errors. We also reveal theta-gamma oscillatory coupling that co-varied with the magnitude of the required adjustment. Our findings open new avenues to probe and extend current theories of performance monitoring by incorporating continuous human interaction tasks and analysis of the ErrP complex rather than individual peaks. © 2023 The Author(s)

关键词： Cognitive neuroscience Neuroscience

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The importance of transparency and reproducibility in artificial intelligence research

arXiv

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arXiv 2020年

作者： Haibe-Kains, Benjamin Adam, George Alexandru Hosny, Ahmed Khodakarami, Farnoosh Waldron, Levi Wang, Bo McIntosh, Chris Kundaje, Anshul Greene, Casey S. Hoffman, Michael M. Leek, Jeffrey T. Huber, Wolfgang Brazma, Alvis Pineau, Joelle Tibshirani, Robert Hastie, Trevor Ioannidis, John P.A. Quackenbush, John Aerts, Hugo J.W.L. Princess Margaret Cancer Centre University Health Network TorontoON Canada Department of Medical Biophysics University of Toronto TorontoON Canada Department of Computer Science University of Toronto TorontoON Canada Ontario Institute for Cancer Research TorontoON Canada Vector Institute for Artificial Intelligence TorontoON Canada Program Brigham and Women's Hospital Harvard Medical School BostonMA United States Radiation Oncology and Radiology Dana-Farber Cancer Institute Brigham and Women's Department of Epidemiology and Biostatistics Institute for Implementation Science in Population Health CUNY Graduate School of Public Health and Health Policy New YorkNY United States Peter Munk Cardiac Centre University Health Network TorontoON Canada Hospital Harvard Medical School BostonMA United States Department of Genetics Stanford University School of Medicine StanfordCA United States Dept. of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Childhood Cancer Data Lab Alex's Lemonade Stand Foundation PhiladelphiaPA United States Department of Biostatistics Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany European Molecular Biology Laboratory European Bioinformatics Institute EMBL-EBI Hinxton United Kingdom McGill University MontrealQC Canada Montreal Institute for Learning Algorithms QC Canada StanfordCA United States Department of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Departments of Medicine of Health Research and Policy and of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Department of Statistics Stanford University School of Humanities and Sciences StanfordCA United States Department of Biostatistics Harvard T.H Chan School of Public Health BostonMA Un

In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening. However, the lack of detailed methods and computer code undermines its scientific value. We identify obstacles hindering transparent and reproducible AI research as faced by McKinney et al and provide solutions with implications for the broader field. Copyright © 2020, The Authors. All rights reserved.

关键词： Artificial intelligence

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