Robots Between Fictions and Facts
Total Page:16
File Type:pdf, Size:1020Kb

Load more
Recommended publications
-
Introduction to Robotics. Sensors and Actuators
Introduction to Computer Vision and Robotics Florian Teich and Tomas Kulvicius* Introduction to Robotics. Sensors and Actuators Large portion of slides are adopted from Florentin Wörgötter, John Hallam and Simon Reich *[email protected] Perception-Action loop Environment Object Eyes Action Perception Arm Brain Perception-Action loop (cont.) Environment Sense Object Cameras Action Perception Robot-arm Computer Act Plan Outline of the course • L1.CV1: Introduction to Computer Vision. Thresholding, Filtering & Connected Coomponents • L2.CV2: Bilateral Filtering, Morphological Operators & Edge Detection • L3.CV3: Corner Detection & Non-Local Filtering • L4.R1: Introduction to Robotics. Sensors and actuators • L5.R2: Movement generation methods • L6.R3: Path planning algorithms • L7.CV4: Line/Circle Detection, Template Matching & Feature Detection • L8.CV5: Segmentation • L9.CV6: Fate Detection, Pedestrian Tracking & Computer Vision in 3D • L10.R4: Robot kinematics and control • L11.R5: Learning algorithms in robotics I: Supervised and unsupervised learning • L12.R6: Learning algorithms in robotics II: Reinforcement learning and genetic algorithms Introduction to Robotics History of robotics • Karel Čapek was one of the most influential Czech writers of the 20th century and a Nobel Prize nominee (1936). • He introduced and made popular the frequently used international word robot, which first appeared in his play R.U.R. (Rossum's Universal Robots) in 1921. 1890-1938 • “Robot” comes from the Czech word “robota”, meaning “forced labor” • Karel named his brother Josef Čapek as the true inventor of the word robot. History of robotics (cont.) • The word "robotics" also comes from science fiction - it first appeared in the short story "Runaround" (1942) by American writer Isaac Asimov. -
Neuro Informatics 2020
Neuro Informatics 2019 September 1-2 Warsaw, Poland PROGRAM BOOK What is INCF? About INCF INCF is an international organization launched in 2005, following a proposal from the Global Science Forum of the OECD to establish international coordination and collaborative informatics infrastructure for neuroscience. INCF is hosted by Karolinska Institutet and the Royal Institute of Technology in Stockholm, Sweden. INCF currently has Governing and Associate Nodes spanning 4 continents, with an extended network comprising organizations, individual researchers, industry, and publishers. INCF promotes the implementation of neuroinformatics and aims to advance data reuse and reproducibility in global brain research by: • developing and endorsing community standards and best practices • leading the development and provision of training and educational resources in neuroinformatics • promoting open science and the sharing of data and other resources • partnering with international stakeholders to promote neuroinformatics at global, national and local levels • engaging scientific, clinical, technical, industry, and funding partners in colla- borative, community-driven projects INCF supports the FAIR (Findable Accessible Interoperable Reusable) principles, and strives to implement them across all deliverables and activities. Learn more: incf.org neuroinformatics2019.org 2 Welcome Welcome to the 12th INCF Congress in Warsaw! It makes me very happy that a decade after the 2nd INCF Congress in Plzen, Czech Republic took place, for the second time in Central Europe, the meeting comes to Warsaw. The global neuroinformatics scenery has changed dramatically over these years. With the European Human Brain Project, the US BRAIN Initiative, Japanese Brain/ MINDS initiative, and many others world-wide, neuroinformatics is alive more than ever, responding to the demands set forth by the modern brain studies. -
For Biological Systems, Maintaining Essential Variables Within Viability Limits Is Not Passive
Second-Order Cybernetics Maintaining Essential Variables Is Not Passive Matthew Egbert « 8 » Proponents of the enactive ap- sible failure to do so will necessarily result “ the survival standard itself is inadequate for proach agree that the homeostat’s double in the homeostat’s death” (§10). However, at the evaluation of life. If mere assurance of per- feedback architecture made an important the level of its physical body, an organism is manence were the point that mattered, life should contribution, but at the same time they always far-from-equilibrium with respect to not have started out in the first place. It is essen- struggle to overcome its limitations (Ike- its environment. Falling into an equilibrium tially precarious and corruptible being, an ad- gami & Suzuki 2008). Franchi briefly refers is the same as dying, because the organism venture in mortality, and in no possible form as to evolutionary robotics models inspired by would lose its ability to do the work of self- assured of enduring as an inorganic body can be. this ultrastability mechanism, but he does producing its own material identity, i.e., the Not duration as such, but ‘duration of what?’ is the not mention that further progress has been very process that ensures that the double question.” (Jonas 2001: 106) difficult. Ezequiel Di Paolo (2003) showed feedback loop between behavior and inter- that implementing Ashby’s mechanism is a nal homeostasis is intrinsically connected Tom Froese graduated with a D.Phil. in cognitive science significant step toward more organism-like within a whole. Only non-living matter can from the Univ. -
1 Jeffrey Gu, Nicolas Wilmer Professor Evan Donahue ISS390S 12/7/18 the Evolution of the Roomba and the AI Field Recently, the C
1 Jeffrey Gu, Nicolas Wilmer Professor Evan Donahue ISS390S 12/7/18 The Evolution of the Roomba and the AI Field Recently, the consumer marketplace has been flooded with a number of intelligent technologies, including many that integrate virtual assistants such as Amazon’s Alexa and Apple’s Siri. Even household appliances like Samsung’s newest refrigerators integrate “smart” technologies. For many in the tech world, artificial intelligence (AI) has long represented the future of cybernetics and technology. For many years, however, AI technologies were not marketed to the average consumer. Rather, most AI companies were focused on corporate, military and governmental applications. Thus, there seemed to be a marked difference in expectations for AI between ordinary people and those involved in the AI field. When asked about the potential of AI, many people would passionately discuss its future applications in education and healthcare.1 However, Mitch Waldrop during a panel discussion featuring several prominent figures in the AI field noted that these applications “seem to be in roughly the inverse priority to what AI people give these subjects.”2 This disconnect began to disappear in 2002, when iRobot, a company founded in 1990 by three former MIT AI Lab scientists, released the Roomba, a robot that autonomously roams and cleans the floors of your house. The commercial success of the Roomba and its business model brought the AI conversation to households around the world. 1 McDermott, Drew, M. Mitchell Waldrop, B. Chandrasekaran, John McDermott, and Roger Schank. 1985. “The Dark Ages of AI: A Panel Discussion at AAAI-84.” AI Magazine 6 (3): 122–122. -
Intelligence Without Reason
Intelligence Without Reason Rodney A. Brooks MIT Artificial Intelligence Lab 545 Technology Square Cambridge, MA 02139, USA Abstract certain modus operandi over the years, which includes a particular set of conventions on how the inputs and out- Computers and Thought are the two categories puts to thought and reasoning are to be handled (e.g., that together define Artificial Intelligence as a the subfield of knowledge representation), and the sorts discipline. It is generally accepted that work in of things that thought and reasoning do (e.g,, planning, Artificial Intelligence over the last thirty years problem solving, etc.). 1 will argue that these conven has had a strong influence on aspects of com- tions cannot account for large aspects of what goes into puter architectures. In this paper we also make intelligence. Furthermore, without those aspects the va the converse claim; that the state of computer lidity of the traditional Artificial Intelligence approaches architecture has been a strong influence on our comes into question. I will also argue that much of the models of thought. The Von Neumann model of landmark work on thought has been influenced by the computation has lead Artificial Intelligence in technological constraints of the available computers, and particular directions. Intelligence in biological thereafter these consequences have often mistakenly be systems is completely different. Recent work in come enshrined as principles, long after the original im behavior-based Artificial Intelligence has pro petus has disappeared. duced new models of intelligence that are much closer in spirit to biological systems. The non- From an evolutionary stance, human level intelligence Von Neumann computational models they use did not suddenly leap onto the scene. -
Neurorobotics Lecture
Neurorobotics An introduction Marc-Oliver Gewaltig In this lecture you’ll learn 1. What is Neurorobotics 2. Examples of simple neurorobots 1. attraction and avoidance 2. reflexes vs. learned behavior 3. The sensory-motor loop 4. Learning in neurorobotics 1. unsupervised learning for sensory representations 2. reinforcement learning for action learning What is Neurorobotics Neurorobotics, is the combined study of neuroscience, robotics, and artificial intelligence. It is the science and technology of embodied autonomous neural systems. https://en.wikipedia.org/wiki/Neurorobotics Neurorobotics: Embodied in silico neuroscience Spinal Cord Reconstructed Reflexes spinal cord/ CDPs brain models Embodiment and virtual environments Musculo-skeletal system – compliant actuators and mechanics Starting simple: Valentino Braitenberg’s Vehicles Valentino Braitenberg (1926-2011) Braitenberg, V. (1984). Vehicles: Experiments in Photo: Alfred Wegener, commons.wikimedia.org synthetic psychology. Cambridge, MA: MIT Press. Vehicle 1 1 Vehicle 2a 1 2a Vehicle 2b 1 2a 2b Vehicle 3 1 2a 2b 3 Vehicle 3 1 2a 2b 3 Exercise How will vehicle 3 move? Generalizing the Braitenberg vehicle Exercise Using weights in {-1,+1}, which weight configurations implement the vehicles 2a, 2b, and 2c? speed light Biological and Non-biological bodies Sensors: cameras, microphones, etc Artificial brain with neurons Servo motors with wheels Biological and Non-biological bodies Sensors: cameras, microphones, etc encode Artificial brain with neurons Servo motors with wheels Biological and Non-biological bodies Sensors: cameras, microphones, etc encode Artificial brain with neurons Servo motors with wheels decode Perception Action Vision Behaviors Hearing Smell Action Central pattern generators Touch Perception Reflexes Temperature Vestibular Muscle contraction Proprioception Perception Short-term Long-term Action memory memory Drives & Working Vision Cognitive Motivation memory control Action Behaviors Sensor Reward & selection Hearing fusion punish. -
The Cybernetic Brain
THE CYBERNETIC BRAIN THE CYBERNETIC BRAIN SKETCHES OF ANOTHER FUTURE Andrew Pickering THE UNIVERSITY OF CHICAGO PRESS CHICAGO AND LONDON ANDREW PICKERING IS PROFESSOR OF SOCIOLOGY AND PHILOSOPHY AT THE UNIVERSITY OF EXETER. HIS BOOKS INCLUDE CONSTRUCTING QUARKS: A SO- CIOLOGICAL HISTORY OF PARTICLE PHYSICS, THE MANGLE OF PRACTICE: TIME, AGENCY, AND SCIENCE, AND SCIENCE AS PRACTICE AND CULTURE, A L L PUBLISHED BY THE UNIVERSITY OF CHICAGO PRESS, AND THE MANGLE IN PRAC- TICE: SCIENCE, SOCIETY, AND BECOMING (COEDITED WITH KEITH GUZIK). THE UNIVERSITY OF CHICAGO PRESS, CHICAGO 60637 THE UNIVERSITY OF CHICAGO PRESS, LTD., LONDON © 2010 BY THE UNIVERSITY OF CHICAGO ALL RIGHTS RESERVED. PUBLISHED 2010 PRINTED IN THE UNITED STATES OF AMERICA 19 18 17 16 15 14 13 12 11 10 1 2 3 4 5 ISBN-13: 978-0-226-66789-8 (CLOTH) ISBN-10: 0-226-66789-8 (CLOTH) Library of Congress Cataloging-in-Publication Data Pickering, Andrew. The cybernetic brain : sketches of another future / Andrew Pickering. p. cm. Includes bibliographical references and index. ISBN-13: 978-0-226-66789-8 (cloth : alk. paper) ISBN-10: 0-226-66789-8 (cloth : alk. paper) 1. Cybernetics. 2. Cybernetics—History. 3. Brain. 4. Self-organizing systems. I. Title. Q310.P53 2010 003’.5—dc22 2009023367 a THE PAPER USED IN THIS PUBLICATION MEETS THE MINIMUM REQUIREMENTS OF THE AMERICAN NATIONAL STANDARD FOR INFORMATION SCIENCES—PERMA- NENCE OF PAPER FOR PRINTED LIBRARY MATERIALS, ANSI Z39.48-1992. DEDICATION For Jane F. CONTENTS Acknowledgments / ix 1. The Adaptive Brain / 1 2. Ontological Theater / 17 PART 1: PSYCHIATRY TO CYBERNETICS 3. -
Boden Grey Walter's
Grey Walter’s Anticipatory Tortoises Margaret Boden Grey Walter and the Ratio Club The British physiologist William Grey Walter (1910–1977) was an early member of the interdisciplinary Ratio Club. This was a small dining club that met several times a year from 1949 to 1955, with a nostalgic final meeting in 1958, at London’s National Hospital for Neurological Diseases. The founder-secretary was the neurosurgeon John Bates, who had worked (alongside the psychologist Kenneth Craik) on servomechanisms for gun turrets during the war. The club was a pioneering source of ideas in what Norbert Wiener had recently dubbed ‘cybernetics’.1 Indeed, Bates’ archive shows that the letter inviting membership spoke of ‘people who had Wiener’s ideas before Wiener’s book appeared’.2 In fact, its founders had considered calling it the Craik Club, in memory of Craik’s work—not least, his stress on ‘synthetic’ models of psychological theories.3 In short, the club was the nucleus of a thriving British tradition of cybernetics, started independently of the transatlantic version. The Ratio members—about twenty at any given time—were a very carefully chosen group. Several of them had been involved in wartime signals research or intelligence work at Bletchley Park, where Alan Turing had used primitive computers to decipher the Nazis’ Enigma code.4 They were drawn from a wide range of disciplines: clinical psychiatry and neurology, physiology, neuroanatomy, mathematics/statistics, physics, astrophysics, and the new areas of control engineering and computer science.5 The aim was to discuss novel ideas: their own, and those of guests—such as Warren McCulloch. -
Kunst Und Kybernetik“
DIPLOMARBEIT Titel der Diplomarbeit „Kunst und Kybernetik“ Verfasserin Felicia Hönig angestrebter akademischer Grad Magistra der Philosophie (Mag. phil.) Wien, im September 2008 Studienkennzahl lt. Studienblatt A296 Studienrichtung lt. Studienblatt Philosophie Betreuer: Ao. Univ. –Prof. Dr. Herbert Hrachovec Inhalt Vorwort .......................................................................................5 1. Einleitung................................................................................7 2. Kybernetik, der Anfang.........................................................11 2.1. Der Begriff .................................................................................12 2.2. Norbert Wiener ..........................................................................14 2.3. Die Bausteine des Theorie-Werdens, drei Publikationen aus den 1940er Jahren...........................................................................15 2.3.1. Norbert Wiener, Arturo Rosenblueth, and Julian Bigelow , 1943, „Behavior, Purpose and Teleology“, Philosophy of Science 10: 18-24. ........................................................................................................ 15 2.3.2. Warren McCulloch, Walter Pitts: „A Logical Calculus of the Ideas Immanent in Nervous Activity“. In: Bulletin of Mathematical Biophysics 5 (1943) 115-133........................................................... 18 2.3.3. C. E. Shannon, „A mathematical theory of communication“, Bell System Technical Journal, vol. 27, pp. 379-423 and 623-656, July and October, -
Was a Neurologist with a Vast Knowledge Including a Solid Scientific and Engineering Training
Scientiae Mathematicae Japonicae Online, e-2006, 861–863 861 ON LAWRENCE STARK AND BIOMEDICAL ENGINEERING Shiro Usui Received March 27, 2006 Abstract. We provide a brief overview of Lawrence Stark’s fundamental contribu- tions to Biomedical Engineering. Lawrence Stark (1926–2004) was a neurologist with a vast knowledge including a solid scientific and engineering training. He pioneered in what is nowadays sometimes called Biomedical Engineering, with an emphasis on the quantitative description of human brain control of movement and vision. In this note, I would like to outline some of his main achievements. Further information may be found, e.g., in [1, 2, 3] and references therein. Stark was interested in science since early childhood. He obtained B.A. at Columbia in 1945, where he majored in English, biology and zoology in only one year and a half. During the war he enrolled in the Navy, who sent him to Albany Medical College, where he earned MD in 1948. He was later awarded Sc.D.h.c. at SUNY in 1988 and Ph.D.h.c. at Tokushima University, Japan, in 1992. He received the Morlock Award in Biomedical Engineering in 1977, and the Franklin V. Taylor Award, Systems Man and Cybernetics Society in 1989. Lawrence Stark held faculty positions at Yale, 1954–1960, as an Assistant Professor of Medicine (Neurology) and Associate Physician; at Massachusetts Institute of Technology, 1960–1965, as Head of the Neurology Section of the Center for Communication Sciences, Electronic Systems Laboratory and Research Laboratory for Electronics; at the University of Illinois at Chicago Circle, 1965–1968, as Professor of Bioengineering, Neurology and Physiology and Chairman of the Biomedical Engineering Department at the Presbyterian St. -
The Biological Turn in Computation, Communication, and Control
Vital Networks: The Biological Turn in Computation, Communication, and Control By Sandra Robinson A thesis submitted to the Graduate Program in Sociology in conformity with the requirements for the Degree of Doctor of Philosophy Queen’s University Kingston, Ontario, Canada January 2014 Copyright © Sandra Robinson, 2014 Abstract Networks, such as the Internet, are comprised of dense information flows with expansive, multi-directional reach that continuously change—and this changeability is what keeps the network active, relative, and vital. I call the form of network exhibiting those dynamic features the vital network. This form of network is not simply the outcome of connectivity and communication between diverse affiliative objects and actors such as cell phones and humans that together convey a sense or feeling of ‘aliveness;’ it is the outcome of deliberate software programming goals for communication systems inspired by nonhuman, self-organizing biological life. The biological turn in computation produces an organizing logic for the vital network that self-propagates connections and disconnections, services, collectives, and structures proximal to forms that feel vital and dynamic. The vital network can do things, it has capacities to act, and different material consequences emerge out of the organization and coordination of communication with particular implications for human privacy, autonomy, and network transparency. In this dissertation, I examine the biological turn in computing as a crucial feature within a development program for the design of digital network control systems that rely on self-regulation and autonomous communication processes intentionally constructed to be non-transparent—to be unseen. I explore nonhuman models of control as a response to this requirement considered through three objects: microbe, simulation, and control, each understood in process terms that disclose what these things do and how they act. -
The First Biologically Inspired Robots Owen Holland
Robotica (2003) volume 21, pp. 351–363. © 2003 Cambridge University Press DOI: 10.1017/S0263574703004971 Printed in the United Kingdom The first biologically inspired robots Owen Holland Department of Computer Science, University of Essex, Wivenhoe Park, Colchester CO4 3SQ (UK). E-mail: [email protected] (Received in Final Form: June 15, 2002) SUMMARY Star. After attending Westminster School, he studied Thie first biologically inspired robots, the famous electro- physiology at Cambridge, and began his research career in mechanical tortoises, were designed and built in 1949 by W. neurophysiology with a dissertation on ‘Conduction in Grey Walter. This paper reviews their origins in Walter’s Nerve and Muscle’. In 1935 he became interested in the new theories of the brain and the nature of life, and uses area of electroencephalography, and quickly showed a talent contemporary unpublished notes and photographs to assess for developing new electronic equipment, which he used to their significance then and now. make a series of fundamental technical and clinical discoveries. In 1939 he was appointed Director of Physiol- ogy at the newly founded Burden Neurological Institute KEYWORDS: First biological robots; Robot tortoises. (BNI) in Bristol; he remained there for the rest of his career. His war work involved him in a variety of activities, including the development of radar, and the use of humans INTRODUCTION in control loops. After the war he recruited a brilliant group Most articles and books on the history of robotics make only 1,2,3 of ex-radar engineers to work with him at the BNI; their a passing reference to Grey Walter’s robot tortoises, technical expertise, combined with Grey Walter’s vision and regarding them as quaint period curiosities quite unrelated creativity, made a major contribution to the development of to our intellectually and technically sophisticated modern electroencephalography over the next twenty years.