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Evaluation of an Industrial Robotic Assistant in an Ecological Environment

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Evaluation of an Industrial Robotic Assistant in an Ecologic...

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Baptiste Busch Giuseppe Cotugno Mahdi Khoramshahi Grigorios Skaltsas Dario Turchi Leonardo Urbano Mirko Wächter You Zhou Tamim Asfour Graham Deacon Duncan Russell Aude Billard EPFL Learning Algorithms and Systems Laboratory (LASA) Lausanne Switzerland Robotics Research Group of Ocado Technology Hatfield UK University of Hertfordshire Hatfield UK KIT High Performance Humanoid Technologies Lab (H2T) Karlsruhe Germany

Social robotic assistants have been widely studied and deployed as telepresence tools or caregivers. Evaluating their design and impact on the people interacting with them is of prime importance. In this research, we evaluate the usability and impact of ARMAR-6, an industrial robotic assistant for maintenance tasks. For this evaluation, we have used a modified System Usability Scale (SUS) to assess the general usability of the robotic system and the Godspeed questionnaire series for the subjective perception of the coworker. We have also recorded the subjects' gaze fixation patterns and analyzed how they differ when working with the robot compared to a human partner.

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An Early Stage Researcher's Primer on systems Medicine Terminology

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Network and Systems Medicine

Network and systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

Recurrent kalman networks: Factorized inference in high-dimensional deep feature spaces

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arXiv 2019年

作者： Becker, Philipp Pandya, Harit Gebhardt, Gregor Zhao, Cheng Taylor, James Neumann, Gerhard Computational Learning for Autonomous Systems TU Darmstadt Darmstadt Germany Bosch Center for Artificial Intelligence Renningen Germany University of Tübingen Tübingen Germany Lincoln Center for Autonomous Systems University of Lincoln Lincoln United Kingdom Extreme Robotics Lab University of Birmingham Birmingham United Kingdom Engineering Department Lancaster University Lancaster United Kingdom

In order to integrate uncertainty estimates into deep time-series modelling, Kalman Filters (KFs) (Kalman et al., 1960) have been integrated with deep learning models, however, such approaches typically rely on approximate inference techniques such as variational inference which makes learning more complex and often less scalable due to approximation errors. We propose a new deep approach to Kalman filtering which can be learned directly in an end-to-end manner using backpropagation without additional approximations. Our approach uses a high-dimensional factorized latent state representation for which the Kalman updates simplify to scalar operations and thus avoids hard to backpropagate, computationally heavy and potentially unstable matrix inversions. Moreover, we use locally linear dynamic models to efficiently propagate the latent state to the next time step. The resulting network architecture, which we call Recurrent Kalman Network (RKN), can be used for any time-series data, similar to a LSTM (Hochreiter & Schmidhuber, 1997) but uses an explicit representation of uncertainty. As shown by our experiments, the RKN obtains much more accurate uncertainty estimates than an LSTM or Gated Recurrent Units (GRUs) (Cho et al., 2014) while also showing a slightly improved prediction performance and outperforms various recent generative models on an image imputation task. Copyright © 2019, The Authors. All rights reserved.

关键词： Kalman filters

Safe Stop Trajectory Planning for Highly Automated Vehicles: An Optimal Control Problem Formulation

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Safe Stop Trajectory Planning for Highly Automated Vehicles:...

2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Svensson, Lars Masson, Lola Mohan, Naveen Ward, Erik Brenden, Anna Pernestål Feng, Lei Törngren, Martin Mechatronics and Embedded Control Systems KTH Royal Institute of Technology StockholmSE-10044 Sweden LAAS-CNRS University of Toulouse Toulouse France Robotics Perception and Learning KTH Royal Institute of Technology Sweden Integrated Transport Research Lab KTH Royal Institute of Technology Sweden

ISBN: (纸本)9781538644522

Highly automated road vehicles need the capability of stopping safely in a situation that disrupts continued normal operation, e.g. due to internal system faults. Motion planning for safe stop differs from nominal motion planning, since there is not a specific goal location. Rather, the desired behavior is that the vehicle should reach a stopped state, preferably outside of active lanes. Also, the functionality to stop safely needs to be of high integrity. The first contribution of this paper is to formulate the safe stop problem as a benchmark optimal control problem, which can be solved by dynamic programming. However, this solution method cannot be used in real-time. The second contribution is to develop a real-time safe stop trajectory planning algorithm, based on selection from a precomputed set of trajectories. By exploiting the particular properties of the safe stop problem, the cardinality of the set is decreased, making the algorithm computationally efficient. Furthermore, a monitoring based architecture concept is proposed, that ensures dependability of the safe stop function. Finally, a proof of concept simulation using the proposed architecture and the safe stop trajectory planner is presented. © 2018 IEEE.

关键词： Motion planning

Forecasting intracranial hypertension using multi-scale waveform metrics

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arXiv 2019年

作者： Hüser, Matthias Kündig, Adrian Karlen, Walter De Luca, Valeria Jaggi, Martin Biomedical Informatics Group Department of Computer Science ETH Zürich Universitätstrasse 6 Zürich8092 Switzerland Mobile Health Systems Lab Institute of Robotics and Intelligent Systems Department of Health Sciences and Technology ETH Zürich Zürich8008 Switzerland Department of Information Technology and Electrical Engineering ETH Zürich Zürich8092 Switzerland Machine Learning & Optimization Lab EPFL Lausanne1015 Switzerland

Objective: Acute intracranial hypertension is an important risk factor of secondary brain damage after traumatic brain injury. Hypertensive episodes are often diagnosed reactively, leading to late detection and lost time for intervention planning. A pro-active approach that predicts critical events several hours ahead of time could assist in directing attention to patients at risk. Approach: We developed a prediction framework that forecasts onsets of acute intracranial hypertension in the next 8 hours. It jointly uses cerebral auto-regulation indices, spectral energies and morphological pulse metrics to describe the neurological state of the patient. One-minute base windows were compressed by computing signal metrics, and then stored in a multi-scale history, from which physiological features were derived. Main results: Our model predicted events up to 8 hours in advance with alarm recall rates of 90% at a precision of 30.3% in the MIMIC-III waveform database, improving upon two baselines from the literature. We found that features derived from high-frequency waveforms substantially improved the prediction performance over simple statistical summaries of low-frequency time serie1s, and each of the three feature classes contributed to the performance gain. The inclusion of long-term history up to 8 hours was especially important. Significance: Our results highlight the importance of information contained in high-frequency waveforms in the neurological intensive care unit. They could motivate future studies on pre-hypertensive patterns and the design of new alarm algorithms for critical events in the injured brain. Copyright © 2019, The Authors. All rights reserved.

关键词： Morphology

Probabilistic Recurrent State-Space Models

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arXiv 2018年

作者： Doerr, Andreas Daniel, Christian Schiegg, Martin Nguyen-Tuong, Duy Schaal, Stefan Toussaint, Marc Trimpe, Sebastian Bosch Center for Artificial Intelligence Renningen Germany Max Planck Institute for Intelligent Systems Stuttgart/Tübingen Germany Computational Learning and Motor Control Lab University of Southern California United States Machine Learning and Robotics Lab University of Stuttgart Germany

State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification. Deterministic versions of SSMs (e.g. LSTMs) proved extremely successful in modeling complex time series data. Fully probabilistic SSMs, however, are often found hard to train, even for smaller problems. To overcome this limitation, we propose a novel model formulation and a scalable training algorithm based on doubly stochastic variational inference and Gaussian processes. In contrast to existing work, the proposed variational approximation allows one to fully capture the latent state temporal correlations. These correlations are the key to robust training. The effectiveness of the proposed PR-SSM is evaluated on a set of realworld benchmark datasets in comparison to stateof-the-art probabilistic model learning methods. Scalability and robustness are demonstrated on a high dimensional problem. Copyright © 2018, The Authors. All rights reserved.

关键词： State space methods

Author Correction: Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control

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Scientific reports 2020年第1期10卷 6694页

作者： Elmar Rueckert Jernej Čamernik Jan Peters Jan Babič Intelligent Autonomous Systems Lab Technische Universität Darmstadt Darmstadt 64289 Germany. rueckert@ias.tu-darmstadt.de. Department for Automation Biocybernetics and Robotics Jožef Stefan Institute Ljubljana SI-1000 Slovenia. Jožef Stefan International Postgraduate School Jamova cesta 39 1000 Ljubljana Slovenia. Intelligent Autonomous Systems Lab Technische Universität Darmstadt Darmstadt 64289 Germany. Robot Learning Group Max-Planck Institute for Intelligent Systems Tuebingen 72076 Germany.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Safe Stop Trajectory Planning for Highly Automated Vehicles: An Optimal Control Problem Formulation

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Safe Stop Trajectory Planning for Highly Automated Vehicles:...

IEEE Symposium on Intelligent Vehicle

作者： Lars Svensson Lola Masson Naveen Mohan Erik Ward Anna Pernestål Brenden Lei Feng Martin Törngren Mechatronics and Embedded Control Systems KTH Royal Institute of Technology Stockholm Sweden LAAS-CNRS University of Toulouse Toulouse France Robotics Perception and Learning KTH Royal Institute of Technology Integrated Transport Research Lab KTH Royal Institute of Technology

关键词： Trajectory Planning Safety Monitoring Computer architecture Optimization Vehicle dynamics

learning task-specific dynamics to improve whole-body control

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arXiv 2018年

作者： Gams, Andrej Mason, Sean A. Ude, Aleš Schaal, Stefan Righetti, Ludovic Humanoid and Cognitive Robotics Lab Dept. of Automatics Bio-cybernetics and Robotics Jožef Stefan Institute Ljubljana Slovenia Computational Learning and Motor Control Lab University of Southern California Los AngelesCA United States Tandon School of Engineering New York University New York United States Max Planck Institute for Intelligent Systems Tuebingen Germany

In task-based inverse dynamics control, reference accelerations used to follow a desired plan can be broken down into feedforward and feedback trajectories. The feedback term accounts for tracking errors that are caused from inaccurate dynamic models or external disturbances. On underactuated, free-floating robots, such as humanoids, good tracking accuracy often necessitates high feedback gains, which leads to undesirable stiff behaviors. The magnitude of these gains is anyways often strongly limited by the control bandwidth. In this paper, we show how to reduce the required contribution of the feedback controller by incorporating learned task-space reference accelerations. Thus, we i) improve the execution of the given specific task, and ii) offer the means to reduce feedback gains, providing for greater compliance of the system. In contrast to learning task-specific joint-torques, which might produce a similar effect but can lead to poor generalization, our approach directly learns the task-space dynamics of the center of mass of a humanoid robot. Simulated and real-world results on the lower part of the Sarcos Hermes humanoid robot demonstrate the applicability of the approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Feedback