2012 IEEE International Conference on Robotics and Automation (ICRA 2012)
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Modelling User Preference for Embodied Artificial Intelligence and Appearance in Realistic Humanoid Robots
Article Modelling User Preference for Embodied Artificial Intelligence and Appearance in Realistic Humanoid Robots Carl Strathearn 1,* and Minhua Ma 2,* 1 School of Computing and Digital Technologies, Staffordshire University, Staffordshire ST4 2DE, UK 2 Falmouth University, Penryn Campus, Penryn TR10 9FE, UK * Correspondence: [email protected] (C.S.); [email protected] (M.M.) Received: 29 June 2020; Accepted: 29 July 2020; Published: 31 July 2020 Abstract: Realistic humanoid robots (RHRs) with embodied artificial intelligence (EAI) have numerous applications in society as the human face is the most natural interface for communication and the human body the most effective form for traversing the manmade areas of the planet. Thus, developing RHRs with high degrees of human-likeness provides a life-like vessel for humans to physically and naturally interact with technology in a manner insurmountable to any other form of non-biological human emulation. This study outlines a human–robot interaction (HRI) experiment employing two automated RHRs with a contrasting appearance and personality. The selective sample group employed in this study is composed of 20 individuals, categorised by age and gender for a diverse statistical analysis. Galvanic skin response, facial expression analysis, and AI analytics permitted cross-analysis of biometric and AI data with participant testimonies to reify the results. This study concludes that younger test subjects preferred HRI with a younger-looking RHR and the more senior age group with an older looking RHR. Moreover, the female test group preferred HRI with an RHR with a younger appearance and male subjects with an older looking RHR. -
September-2019-E-Magazine.Pdf
Jatin Verma’s Current Affairs Magazine (September, 2019) Visit:- www.jatinverma.org 1 2 Note: Our magazine covers important current affairs from all the important sources referred by UPSC CSE aspirants- The Hindu, Indian Express, PIB, RSTV, LSTV, Economic & Political Weekly and Frontline magazine and other journals. Since we do not want to compromise on quality of facts & analysis, the magazine might run into some extra pages. We assure you that we have tried our best to make this magazine the “one stop solution” for your current affairs preparation for UPSC CSE 2020. 3 FOCUS ARTICLES Economic Slowdown India’s gross domestic product (GDP) growth rate slowed to a six-year low of 5% in the first quarter of the 2019-20 financial year, led by a dramatic slowdown in the manufacturing sector, according to GDP data released by the National Statistical Office (NSO). ● The growth of Gross Value Added (GVA) stood at 4.9% in the first quarter of the financial year 2019- 20, also the slowest in six years. ● Manufacturing sector grew at an anaemic two-year low of 0.6% in the first quarter of 2019-20, down from 12.1% in the same quarter of the previous year. ● Automobile Sector has as well reported a high double-digit decline in their sales in August as it continued to reel under one of the worst slowdowns in its history. ● Agriculture sector also saw a dramatic slowdown in growth to 2% from 5.1% over the same period. ● Real estate sector was also highlighted by the slowdown in its growth rate to 5.7% in the first quarter of this financial year, compared with 9.6% in the same quarter of 2018-19. -
Pdf • Cynthia Breazeal
© copyright by Christoph Bartneck, Tony Belpaeime, Friederike Eyssel, Takayuki Kanda, Merel Keijsers, and Selma Sabanovic 2019. https://www.human-robot-interaction.org Human{Robot Interaction An Introduction Christoph Bartneck, Tony Belpaeme, Friederike Eyssel, Takayuki Kanda, Merel Keijsers, Selma Sabanovi´cˇ This material has been published by Cambridge University Press as Human Robot Interaction by Christoph Bartneck, Tony Belpaeime, Friederike Eyssel, Takayuki Kanda, Merel Keijsers, and Selma Sabanovic. ISBN: 9781108735407 (http://www.cambridge.org/9781108735407). This pre-publication version is free to view and download for personal use only. Not for re-distribution, re-sale or use in derivative works. © copyright by Christoph Bartneck, Tony Belpaeime, Friederike Eyssel, Takayuki Kanda, Merel Keijsers, and Selma Sabanovic 2019. https://www.human-robot-interaction.org This material has been published by Cambridge University Press as Human Robot Interaction by Christoph Bartneck, Tony Belpaeime, Friederike Eyssel, Takayuki Kanda, Merel Keijsers, and Selma Sabanovic. ISBN: 9781108735407 (http://www.cambridge.org/9781108735407). This pre-publication version is free to view and download for personal use only. Not for re-distribution, re-sale or use in derivative works. © copyright by Christoph Bartneck, Tony Belpaeime, Friederike Eyssel, Takayuki Kanda, Merel Keijsers, and Selma Sabanovic 2019. https://www.human-robot-interaction.org Contents List of illustrations viii List of tables xi 1 Introduction 1 1.1 About this book 1 1.2 Christoph -
Mapping Why Mapping?
Mapping Why Mapping? Learning maps is one of the fundamental problems in mobile robotics Maps allow robots to efficiently carry out their tasks, allow localization … Successful robot systems rely on maps for localization, path planning, activity planning etc. The General Problem of Mapping What does the environment look like? The General Problem of Mapping The problem of robotic mapping is that of acquiring a spatial model of a robot’s environment Formally, mapping involves, given the sensor data, d {u1, z1,u2 , z2 ,,un , zn} to calculate the most likely map m* arg max P(m | d) m Mapping Challenges A key challenge arises from the measurement errors, which are statistically dependent Errors accumulate over time They affect future measurement Other Challenges of Mapping The high dimensionality of the entities that are being mapped Mapping can be high dimensional Correspondence problem Determine if sensor measurements taken at different points in time correspond to the same physical object in the world Environment changes over time Slower changes: trees in different seasons Faster changes: people walking by Robots must choose their way during mapping Robotic exploration problem Factors that Influence Mapping Size: the larger the environment, the more difficult Perceptual ambiguity the more frequent different places look alike, the more difficult Cycles cycles make robots return via different paths, the accumulated odometric error can be huge The following discussion assumes mapping with known poses Mapping vs. Localization Learning maps is a “chicken-and-egg” problem First, there is a localization problem. Errors can be easily accumulated in odometry, making it less certain about where it is Methods exist to correct the error given a perfect map Second, there is a mapping problem. -
(12) United States Patent (10) Patent No.: US 8,337,482 B2 Wood, Jr
US008337482B2 (12) United States Patent (10) Patent No.: US 8,337,482 B2 Wood, Jr. (45) Date of Patent: *Dec. 25, 2012 (54) SYSTEM FOR PERFUSION MANAGEMENT (56) References Cited (75) Inventor: Lowell L. Wood, Jr., Livermore, CA U.S. PATENT DOCUMENTS (US) 3,391,697 A 7, 1968 Greatbatch 3,821,469 A 6, 1974 Whetstone et al. (73) Assignee: The Invention Science Fund I, LLC, 3,941,1273,837,339 A 3,9, 19761974 flagAisenb e tal. Bellevue, WA (US) 3,983.474. A 9/1976 Kuipers 4,054,881 A 10, 1977 Raab (*)c Notice:- r Subject to any disclaimer, the term of this 4,202,3494,119,900 A 10,5/1980 1978 JonesKremnitz patent is extended or adjusted under 35 4.262.306 A 4, 1981 Renner U.S.C. 154(b) by 1417 days. 4.267,831. A 5/1981 Aguilar This patent is Subject to a terminal dis- 2. A 3.18: R et al. claimer. 4,339.953 A 7, 1982 Iwasaki 4,367,741 A 1/1983 Michaels 4,396,885 A 8, 1983 Constant (21) Appl. No.: 10/827,576 4,403,321 A 9/1983 Kriger 4.418,422 A 11/1983 Richter et al. (22) Filed: Apr. 19, 2004 4,431,005 A 2f1984 McCormick 4,583, 190 A 4, 1986 Sab O O 4,585,652 A 4, 1986 Miller et al. (65) Prior Publication Data 4,628,928 A 12/1986 Lowell 4,638,798 A 1/1987 Shelden et al. US 2005/O234399 A1 Oct. -
Simultaneous Localization and Mapping in Marine Environments
Chapter 8 Simultaneous Localization and Mapping in Marine Environments Maurice F. Fallon∗, Hordur Johannsson, Michael Kaess, John Folkesson, Hunter McClelland, Brendan J. Englot, Franz S. Hover and John J. Leonard Abstract Accurate navigation is a fundamental requirement for robotic systems— marine and terrestrial. For an intelligent autonomous system to interact effectively and safely with its environment, it needs to accurately perceive its surroundings. While traditional dead-reckoning filtering can achieve extremely high performance, the localization accuracy decays monotonically with distance traveled. Other ap- proaches (such as external beacons) can help; nonetheless, the typical prerogative is to remain at a safe distance and to avoid engaging with the environment. In this chapter we discuss alternative approaches which utilize onboard sensors so that the robot can estimate the location of sensed objects and use these observations to improve its own navigation as well its perception of the environment. This approach allows for meaningful interaction and autonomy. Three motivating autonomous underwater vehicle (AUV) applications are outlined herein. The first fuses external range sensing with relative sonar measurements. The second application localizes relative to a prior map so as to revisit a specific feature, while the third builds an accurate model of an underwater structure which is consistent and complete. In particular we demonstrate that each approach can be abstracted to a core problem of incremental estimation within a sparse graph of the AUV’s trajectory and the locations of features of interest which can be updated and optimized in real time on board the AUV. Maurice F. Fallon · Hordur Johannsson · Michael Kaess · John Folkesson · Hunter McClelland · Brendan J. -
PT-365-Science-And-Tech-2020.Pdf
SCIENCE AND TECHNOLOGY Table of Contents 1. BIOTECHNOLOGY ___________________ 3 3.11. RFID ___________________________ 29 1.1. DNA Technology (Use & Application) 3.12. Miscellaneous ___________________ 29 Regulation Bill ________________________ 3 4. DEFENCE TECHNOLOGY _____________ 32 1.2. National Guidelines for Gene Therapy __ 3 4.1. Missiles _________________________ 32 1.3. MANAV: Human Atlas Initiative _______ 5 4.2. Submarine and Ships _______________ 33 1.4. Genome India Project _______________ 6 4.3. Aircrafts and Helicopters ____________ 34 1.5. GM Crops _________________________ 6 4.4. Other weapons system _____________ 35 1.5.1. Golden Rice ________________________ 7 4.5. Space Weaponisation ______________ 36 2. SPACE TECHNOLOGY ________________ 8 4.6. Drone Regulation __________________ 37 2.1. ISRO _____________________________ 8 2.1.1. Gaganyaan _________________________ 8 4.7. Other important news ______________ 38 2.1.2. Chandrayaan 2 _____________________ 9 2.1.3. Geotail ___________________________ 10 5. HEALTH _________________________ 39 2.1.4. NaVIC ____________________________ 11 5.1. Viral diseases _____________________ 39 2.1.5. GSAT-30 __________________________ 12 5.1.1. Polio _____________________________ 39 2.1.6. GEMINI __________________________ 12 5.1.2. New HIV Subtype Found by Genetic 2.1.7. Indian Data Relay Satellite System (IDRSS) Sequencing _____________________________ 40 ______________________________________ 13 5.1.3. Other viral Diseases _________________ 40 2.1.8. Cartosat-3 ________________________ 13 2.1.9. RISAT-2BR1 _______________________ 14 5.2. Bacterial Diseases _________________ 40 2.1.10. Newspace India ___________________ 14 5.2.1. Tuberculosis _______________________ 40 2.1.11. Other ISRO Missions _______________ 14 5.2.1.1. Global Fund for AIDS, TB and Malaria42 5.2.2. -
Distributed Robotic Mapping
DISTRIBUTED COLLABORATIVE ROBOTIC MAPPING by DAVID BARNHARD (Under Direction the of Dr. Walter D. Potter) ABSTRACT The utilization of multiple robots to map an unknown environment is a challenging problem within Artificial Intelligence. This thesis first presents previous efforts to develop robotic platforms that have demonstrated incremental progress in coordination for mapping and target acquisition tasks. Next, we present a rewards based method that could increase the coordination ability of multiple robots in a distributed mapping task. The method that is presented is a reinforcement based emergent behavior approach that rewards individual robots for performing desired tasks. It is expected that the use of a reward and taxation system will result in individual robots effectively coordinating their efforts to complete a distributed mapping task. INDEX WORDS: Robotics, Artificial Intelligence, Distributed Processing, Collaborative Robotics DISTRIBUTED COLLABORATIVE ROBOTIC MAPPING by DAVID BARNHARD B.A. Cognitive Science, University of Georgia, 2001 A Thesis Submitted to the Graduate Faculty of The University of Georgia in Partial Fulfillment of the Requirements for the Degree MASTERS OF SCIENCE ATHENS, GEORGIA 2005 © 2005 David Barnhard All Rights Reserved DISTRIBUTED COLLABORATIVE ROBOTIC MAPPING by DAVID BARNHARD Major Professor: Dr. Walter D. Potter Committee: Dr. Khaled Rasheed Dr. Suchendra Bhandarkar Electronic Version Approved: Maureen Grasso Dean of the Graduate School The University of Georgia August 2005 DEDICATION I dedicate this thesis to my lovely betrothed, Rachel. Everyday I wake to hope and wonder because of her. She is my strength and happiness every moment that I am alive. She has continually, but gently kept pushing me to get this project done, and I know without her careful insistence this never would have been completed. -
Creativity in Humans, Robots, Humbots 23
T. Lubart, D. Esposito, A. Gubenko, and C. Houssemand, Creativity in Humans, Robots, Humbots 23 Vol. 8, Issue 1, 2021 Theories – Research – Applications Creativity in Humans, Robots, Humbots Todd Lubart1, 2, Dario Esposito1, Alla Gubenko3, and Claude Houssemand3 1 LaPEA, Université de Paris and Univ. Gustav Eiffel, F-92100 Boulogne-Billancourt, France 2 HSE University, Moscow, Russia 3 Department of Education and Social Work, Institute for Lifelong Learning and Guidance, University of Luxembourg, Luxembourg ABSTRACT This paper examines three ways that robots can inter- KEYWORDS: face with creativity. In particular, social robots which are creation process, human-robot interaction, designed to interact with humans are examined. In the human-robot co-creativity, embodied creativity, first mode, human creativity can be supported by social social robots robots. In a second mode, social robots can be creative agents and humans serve to support robot’s produc- Note: The article was prepared in the framework of a research grant funded by the Ministry of Science and tions. In the third and final mode, there is complemen- Higher Education of the Russian Federation (grant ID: tary action in creative work, which may be collaborative 075-15-2020-928). co-creation or a division of labor in creative projects. Il- lustrative examples are provided and key issues for fur- ther discussion are raised. Article history: Corresponding author at: Received: June 20, 2021 Todd Lubart Received in revised from: July 9, 2021 E-MAIL: [email protected] Accepted: July 9, 2021 ISSN 2354-0036 DOI: 10.2478/ctra-2021-0003 24 Creativity. Theories – Research – Applications, 8(1) 2021 INtrODUctiON Robots are agents equipped with sensors, actuators and effectors which enable them to move and perform manipulative tasks (Russell & Norvig, 2010). -
(12) United States Patent (10) Patent No.: US 8,092,549 B2 Hillis Et Al
USO08092.549B2 (12) United States Patent (10) Patent No.: US 8,092,549 B2 Hillis et al. (45) Date of Patent: Jan. 10, 2012 (54) CILLATED STENT-LIKE-SYSTEM 4,054,881. A 10/1977 Raab 4,119,900 A 10, 1978 Kremnitz (75) Inventors: E.O O i. CA (US, 4,262,3064,202,349 A 4,5/1980 1981 JonesRenner uriel Y. Snkawa, L1Vermore, 4,314,251 A 2f1982 Raab (US); Clarence T. Tegreene, Bellevue, 4,317,078 A 2/1982 Weed et al. WA (US); Richa Wilson, San Francisco, 4,339.953 A 7, 1982 Iwasaki CA (US); Victoria Y. H. Wood 4,367,741 A 1/1983 Michaels Livermore,s CA (US); Lowell L.s Wood, 4,403,3214,396,885 A 9/19838, 1983 KrigerConstant Jr., Livermore, CA (US) 4.418,422 A 1 1/1983 Richter et al. 4,431,005 A 2f1984 McCormick (73) Assignee: The Invention Science Fund I, LLC, 4,583, 190 A 4, 1986 Sab Bellevue, WA (US) 4,585,652 A 4, 1986 Miller et al. 4,628,928 A 12/1986 Lowell (*)c Notice:- r Subject to any disclaimer, the term of this 4,642,7864,638,798 A 2f19871/1987 SheldenHansen et al. patent is extended or adjusted under 35 4,651,732 A 3, 1987 Frederick U.S.C. 154(b) by 210 days. 4,658,214. A 4, 1987 Petersen 4,714.460 A 12/1987 Calderon (21) Appl. No.: 10/949,186 4,717,381 A 1/1988 Papantonakos 4,733,661 A 3, 1988 Palestrant 1-1. -
Semantic Maps for Domestic Robots Electrical and Computer Engineering
Semantic Maps for Domestic Robots João Miguel Camisão Soares de Goyri O’Neill Thesis to obtain the Master of Science Degree in Electrical and Computer Engineering Supervisor(s): Prof. Rodrigo Martins de Matos Ventura Prof. Pedro Daniel dos Santos Miraldo Examination Committee Chairperson: Prof. João Fernando Cardoso Silva Sequeira Supervisor: Prof. Rodrigo Martins de Matos Ventura Member of the Committee: Prof. Plinio Moreno López October 2015 ii Resumo Dado o aumento de aplicac¸oes˜ de robosˆ e particularmente de robosˆ de servic¸o, tem surgido na comu- nidade da Inteligenciaˆ artificial a questao˜ de como gerar comportamento inteligente. Embora se tenha ate´ agora respondido a esta questao˜ com modelos muito completos e r´ıgidos do ambiente. Cada vez mais se aborda o problema com modelos mais simples e que podem aparentar ser incompletos mas que na verdade se controem a` medida que interagem com o ambiente tornando-se progressivamente mais eficientes. Neste trabalho sera´ apresentado um mapa semanticoˆ que tera´ o conhecimento funda- mental para completar a tarefa de determinar a localizac¸ao˜ de objectos no mundo. Esta tarefa utiliza o modulo´ de reconhecimento de objectos para experienciar sensorialmente o ambiente, um planeador de acc¸oes˜ e um mapa semanticoˆ que recebe informac¸ao˜ de baixo n´ıvel do reconhecedor e a converte em informac¸ao˜ de alto n´ıvel para o planeador. A sua architectura foi desenhada tendo em conta que e´ suposto que o mapa semanticoˆ seja utilizado por todos os modulos.´ Varios´ testes foram realizados em cenarios´ realistas e utilizando objectos do dia a dia. As experienciasˆ mostram que o uso do mapa semanticoˆ torna o processo mais eficiente a partir da primeira interac¸ao˜ com o ambiente. -
Arxiv:1606.05830V4
This paper has been accepted for publication in IEEE Transactions on Robotics. DOI: 10.1109/TRO.2016.2624754 IEEE Explore: http://ieeexplore.ieee.org/document/7747236/ Please cite the paper as: C. Cadena and L. Carlone and H. Carrillo and Y. Latif and D. Scaramuzza and J. Neira and I. Reid and J.J. Leonard, “Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age”, in IEEE Transactions on Robotics 32 (6) pp 1309-1332, 2016 bibtex: @articlefCadena16tro-SLAMfuture, title = fPast, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Ageg, author = fC. Cadena and L. Carlone and H. Carrillo and Y. Latif and D. Scaramuzza and J. Neira and I. Reid and J.J. Leonardg, journal = ffIEEE Transactions on Roboticsgg, year = f2016g, number = f6g, pages = f1309–1332g, volume = f32g g arXiv:1606.05830v4 [cs.RO] 30 Jan 2017 1 Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age Cesar Cadena, Luca Carlone, Henry Carrillo, Yasir Latif, Davide Scaramuzza, Jose´ Neira, Ian Reid, John J. Leonard Abstract—Simultaneous Localization And Mapping (SLAM) I. INTRODUCTION consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot LAM comprises the simultaneous estimation of the state moving within it. The SLAM community has made astonishing S of a robot equipped with on-board sensors, and the con- progress over the last 30 years, enabling large-scale real-world struction of a model (the map) of the environment that the applications, and witnessing a steady transition of this technology to industry.