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Controls and Art : Inquiries at the Intersection of the Subjective and the Objective.

By: Laviers, Amy.
Contributor(s): Egerstedt, Magnus.
Material type: TextTextSeries: eBooks on Demand.Publisher: Cham : Springer, 2014Copyright date: ©2014Edition: 1st ed.Description: 1 online resource (238 pages).Content type: text Media type: computer Carrier type: online resourceISBN: 9783319039046.Subject(s): Automatic control -- Congresses.;Digital control systems -- Congresses.;Automation -- CongressesGenre/Form: Electronic books.Additional physical formats: Print version:: Controls and Art : Inquiries at the Intersection of the Subjective and the ObjectiveDDC classification: 629.8/312 LOC classification: TA1-2040Online resources: Click here to view this ebook.
Contents:
Intro -- Preface -- Contents -- 1 Metric Preference Learning with Applications to Motion Imitation -- 1.1 Introduction to Motion Imitation Through Puppetry -- 1.2 Preference Learning -- 1.3 Problem Formulation -- 1.4 The Preference Graph -- 1.5 Metric Costs -- 1.5.1 Bounded Case -- 1.5.2 Unbounded Case: The Minimax-Rate Problem -- 1.6 An Asymptotic Observer for Metric Cost Models -- 1.7 Applications -- 1.7.1 Apples and Oranges -- 1.7.2 Amoebas and Humans -- 1.8 Concluding Remarks -- References -- 2 In the Dance Studio: An Art and Engineering Exploration of Human Flocking -- 2.1 Flock Logic -- 2.2 Human Flocking -- 2.2.1 Explorations -- 2.2.2 FlockMaker -- 2.2.3 Experiments -- 2.3 Trajectory Tracking -- 2.4 Graph Theory and Visualization -- 2.4.1 Background on Graphs -- 2.4.2 Visualization of Graphs -- 2.5 Sensing Model and Graph Computation -- 2.6 Analysis of Individual Influence -- 2.7 Final Remarks -- References -- 3 Dancing Robots: The Control Theory of Communication Through Movement -- 3.1 Dance and Motion Primitives -- 3.2 The Rudiments of Knot Theory -- 3.3 Salsa: Energy, Complexity, and Perceived Artistic Merit -- 3.4 Deconstructing the Dances into Four-Step Phrases -- 3.5 The Topological Knot Theory of Intertwined Arms -- 3.6 Complexity Merit of an Intermediate Level Salsa Performance -- 3.7 Conclusion -- References -- 4 So You Think You Can Dance? Rhythmic Flight Performances with Quadrocopters -- 4.1 Rhythmic Flight with Quadrocopters -- 4.1.1 Vision of a Quadrocopter Dance Performance -- 4.1.2 Artistic Motivation -- 4.1.3 The Interplay of Dance and Technology -- 4.1.4 First Steps Toward a Rhythmic Flight Performance -- 4.2 Quadrocopter Dynamics: How do Quadrocopters Move? -- 4.2.1 Dynamics Model of the Quadrocopter -- 4.2.2 Vehicle Constraints -- 4.2.3 Implications for a Rhythmic Flight Performance.
4.3 Motion Design: What is a Dance Step for a Quadrocopter? -- 4.3.1 Music Analysis -- 4.3.2 Periodic Motions -- 4.4 Motion Feasibility: What are the Physical Limits of a Quadrocopter? -- 4.4.1 Motor Thrust Limits -- 4.4.2 Example: Side-to-Side Motion -- 4.5 Quadrocopter Control: How do Quadrocopters Execute Their Movements? -- 4.5.1 Trajectory-Following Controller -- 4.5.2 Tracking Performance of Periodic Motions -- 4.6 Motion Synchronization: Can a Quadrocopter Move in the Rhythm of the Music? -- 4.6.1 Synchronization: The Basic Idea -- 4.6.2 Synchronization in Three Dimensions -- 4.7 Rhythmic Performances -- 4.7.1 Experimental Testbed -- 4.7.2 Implementation and Robustness -- 4.7.3 Choreographies -- 4.8 Conclusions and Outlook -- References -- 5 Robotic Puppets and the Engineering of Autonomous Theater -- 5.1 Puppets Manipulated by Machines Manipulated by Engineers -- 5.2 Puppets: Esthetic and Mechanical Considerations -- 5.3 Typical Approach -- 5.3.1 Dynamics -- 5.3.2 Nonlinear Optimal Control -- 5.3.3 Choreography and Hybrid Optimal Control -- 5.4 Discrete Time with Scalability -- 5.5 Examples -- 5.5.1 Desired Motion: Simulated Trajectory -- 5.5.2 Desired Motion: Motion Capture Data -- 5.6 Conclusions -- References -- 6 The Artistic Geometry of Consensus Protocols -- 6.1 The Role of Geometric Patterns in the History of Art -- 6.2 A Brief of Consensus Protocols -- 6.3 Motivating Example -- 6.4 Extension to N Agents in the Plane -- 6.5 Periodic and Quasi-Periodic Trajectories -- 6.6 Orbit Pattern Generation -- 6.6.1 A Family of Achievable Paths -- 6.6.2 Illustrative Example: Three Agents -- 6.7 A Gallery of Orbits -- 6.8 Extensions to Pattern Generation on Curved Surfaces -- 6.9 Discussion: The Mathematics of Aesthetics -- 6.10 Conclusions -- References -- 7 Generating Music from Flocking Dynamics -- 7.1 Order, Disorder, Flocks, and Music.
7.2 A Minimal Flocking Algorithm -- 7.3 Software Tools -- 7.4 From Emergent Dynamics to Emerging Sounds -- 7.4.1 The Direct Approach -- 7.4.2 The Coupled Oscillators Approach -- 7.4.3 The Physical Friction Approach -- 7.5 From Emergent Dynamics to Emerging Music -- 7.6 Music and Complex Systems -- References -- 8 Algorithms for Visual Tracking of Visitors Under Variable-Lighting Conditions for a Responsive Audio Art Installation -- 8.1 Installation Concept and Visitor Experience -- 8.1.1 Technical Overview -- 8.1.2 Related Work -- 8.1.3 Notation -- 8.1.4 Assumptions -- 8.1.5 Problem Statement -- 8.2 Probabilistic Foreground Segmentation -- 8.2.1 Quantization -- 8.2.2 Histogram Initialization -- 8.2.3 Bayesian Inference -- 8.2.4 Filtering and Connected Components -- 8.2.5 Updating the Histogram -- 8.3 Multiple Visitor Tracking -- 8.3.1 Gale-Shapley Matching -- 8.3.2 Heuristic Confidence Model -- 8.4 Results -- 8.5 Conclusions -- 8.5.1 Reviews -- 8.6 Acknowledgements -- References -- 9 Style-Based Robotic Motion in Contemporary Dance Performance -- 9.1 The Robot as an Onstage Character -- 9.2 A Description of Movement Style -- 9.3 Interpreting Human Motion for Robotics -- 9.4 Generating Stylized Motion -- 9.4.1 A Quantitative Movement Model -- 9.4.2 Movement Sequencing -- 9.4.3 Movement Modulation -- 9.5 Rule-Based Movement Generation -- 9.6 What did the Audience See?: A Human Study -- 9.7 Conclusions -- References -- Author Index -- Subject Index.
Summary: This first-of-its-kind anthology assembles technical research that makes possible such creations as dancing humanoids, robotic art installations, and music generated by mathematically precise methods. Includes open problems and topics for future research.
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Intro -- Preface -- Contents -- 1 Metric Preference Learning with Applications to Motion Imitation -- 1.1 Introduction to Motion Imitation Through Puppetry -- 1.2 Preference Learning -- 1.3 Problem Formulation -- 1.4 The Preference Graph -- 1.5 Metric Costs -- 1.5.1 Bounded Case -- 1.5.2 Unbounded Case: The Minimax-Rate Problem -- 1.6 An Asymptotic Observer for Metric Cost Models -- 1.7 Applications -- 1.7.1 Apples and Oranges -- 1.7.2 Amoebas and Humans -- 1.8 Concluding Remarks -- References -- 2 In the Dance Studio: An Art and Engineering Exploration of Human Flocking -- 2.1 Flock Logic -- 2.2 Human Flocking -- 2.2.1 Explorations -- 2.2.2 FlockMaker -- 2.2.3 Experiments -- 2.3 Trajectory Tracking -- 2.4 Graph Theory and Visualization -- 2.4.1 Background on Graphs -- 2.4.2 Visualization of Graphs -- 2.5 Sensing Model and Graph Computation -- 2.6 Analysis of Individual Influence -- 2.7 Final Remarks -- References -- 3 Dancing Robots: The Control Theory of Communication Through Movement -- 3.1 Dance and Motion Primitives -- 3.2 The Rudiments of Knot Theory -- 3.3 Salsa: Energy, Complexity, and Perceived Artistic Merit -- 3.4 Deconstructing the Dances into Four-Step Phrases -- 3.5 The Topological Knot Theory of Intertwined Arms -- 3.6 Complexity Merit of an Intermediate Level Salsa Performance -- 3.7 Conclusion -- References -- 4 So You Think You Can Dance? Rhythmic Flight Performances with Quadrocopters -- 4.1 Rhythmic Flight with Quadrocopters -- 4.1.1 Vision of a Quadrocopter Dance Performance -- 4.1.2 Artistic Motivation -- 4.1.3 The Interplay of Dance and Technology -- 4.1.4 First Steps Toward a Rhythmic Flight Performance -- 4.2 Quadrocopter Dynamics: How do Quadrocopters Move? -- 4.2.1 Dynamics Model of the Quadrocopter -- 4.2.2 Vehicle Constraints -- 4.2.3 Implications for a Rhythmic Flight Performance.

4.3 Motion Design: What is a Dance Step for a Quadrocopter? -- 4.3.1 Music Analysis -- 4.3.2 Periodic Motions -- 4.4 Motion Feasibility: What are the Physical Limits of a Quadrocopter? -- 4.4.1 Motor Thrust Limits -- 4.4.2 Example: Side-to-Side Motion -- 4.5 Quadrocopter Control: How do Quadrocopters Execute Their Movements? -- 4.5.1 Trajectory-Following Controller -- 4.5.2 Tracking Performance of Periodic Motions -- 4.6 Motion Synchronization: Can a Quadrocopter Move in the Rhythm of the Music? -- 4.6.1 Synchronization: The Basic Idea -- 4.6.2 Synchronization in Three Dimensions -- 4.7 Rhythmic Performances -- 4.7.1 Experimental Testbed -- 4.7.2 Implementation and Robustness -- 4.7.3 Choreographies -- 4.8 Conclusions and Outlook -- References -- 5 Robotic Puppets and the Engineering of Autonomous Theater -- 5.1 Puppets Manipulated by Machines Manipulated by Engineers -- 5.2 Puppets: Esthetic and Mechanical Considerations -- 5.3 Typical Approach -- 5.3.1 Dynamics -- 5.3.2 Nonlinear Optimal Control -- 5.3.3 Choreography and Hybrid Optimal Control -- 5.4 Discrete Time with Scalability -- 5.5 Examples -- 5.5.1 Desired Motion: Simulated Trajectory -- 5.5.2 Desired Motion: Motion Capture Data -- 5.6 Conclusions -- References -- 6 The Artistic Geometry of Consensus Protocols -- 6.1 The Role of Geometric Patterns in the History of Art -- 6.2 A Brief of Consensus Protocols -- 6.3 Motivating Example -- 6.4 Extension to N Agents in the Plane -- 6.5 Periodic and Quasi-Periodic Trajectories -- 6.6 Orbit Pattern Generation -- 6.6.1 A Family of Achievable Paths -- 6.6.2 Illustrative Example: Three Agents -- 6.7 A Gallery of Orbits -- 6.8 Extensions to Pattern Generation on Curved Surfaces -- 6.9 Discussion: The Mathematics of Aesthetics -- 6.10 Conclusions -- References -- 7 Generating Music from Flocking Dynamics -- 7.1 Order, Disorder, Flocks, and Music.

7.2 A Minimal Flocking Algorithm -- 7.3 Software Tools -- 7.4 From Emergent Dynamics to Emerging Sounds -- 7.4.1 The Direct Approach -- 7.4.2 The Coupled Oscillators Approach -- 7.4.3 The Physical Friction Approach -- 7.5 From Emergent Dynamics to Emerging Music -- 7.6 Music and Complex Systems -- References -- 8 Algorithms for Visual Tracking of Visitors Under Variable-Lighting Conditions for a Responsive Audio Art Installation -- 8.1 Installation Concept and Visitor Experience -- 8.1.1 Technical Overview -- 8.1.2 Related Work -- 8.1.3 Notation -- 8.1.4 Assumptions -- 8.1.5 Problem Statement -- 8.2 Probabilistic Foreground Segmentation -- 8.2.1 Quantization -- 8.2.2 Histogram Initialization -- 8.2.3 Bayesian Inference -- 8.2.4 Filtering and Connected Components -- 8.2.5 Updating the Histogram -- 8.3 Multiple Visitor Tracking -- 8.3.1 Gale-Shapley Matching -- 8.3.2 Heuristic Confidence Model -- 8.4 Results -- 8.5 Conclusions -- 8.5.1 Reviews -- 8.6 Acknowledgements -- References -- 9 Style-Based Robotic Motion in Contemporary Dance Performance -- 9.1 The Robot as an Onstage Character -- 9.2 A Description of Movement Style -- 9.3 Interpreting Human Motion for Robotics -- 9.4 Generating Stylized Motion -- 9.4.1 A Quantitative Movement Model -- 9.4.2 Movement Sequencing -- 9.4.3 Movement Modulation -- 9.5 Rule-Based Movement Generation -- 9.6 What did the Audience See?: A Human Study -- 9.7 Conclusions -- References -- Author Index -- Subject Index.

This first-of-its-kind anthology assembles technical research that makes possible such creations as dancing humanoids, robotic art installations, and music generated by mathematically precise methods. Includes open problems and topics for future research.

Description based on publisher supplied metadata and other sources.

Author notes provided by Syndetics

<p>Amy LaViers is an Assistant Professor at the University of Virginia and the director of the Robotics, Automation, and Dance (RAD) Lab. She completed an undergraduate thesis at Princeton University and a doctoral dissertation at Georgia Inst. of Technology that straddle the world of art and control engineering. Her thesis at Princeton received top thesis prizes and her dissertation at Georgia Tech was accompanied by a contemporary dance show entitled "Automaton." She is the co-organizer of two Invited Sessions (the first of their kind) on Controls and Art at the American Control Conference. She received the ECE Graduate Teaching Excellence Award at Georgia Tech and the Calvin Dodd MacCracken Senior Thesis Prize, Morgan Mckenzie Senior Thesis Prize, and Lyman Page Dance Award at Princeton.</p> <p> </p> <p>Magnus B. Egerstedt is a Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology, where he has been on the faculty since 2001. He also holds an adjunct appointment in the School of Interactive Computing with the College of Computing at Georgia Tech. Magnus Egerstedt received the M.S. degree in Engineering Physics and the Ph.D. degree in Applied Mathematics from the Royal Institute of Technology, Stockholm, Sweden, in 1996 and 2000 respectively, and he received the B.A. degree in Philosophy from Stockholm University in 1996. Dr. Egerstedt's research interests include hybrid and networked control, with applications in motion planning, control and coordination of mobile robots, and he serves as Editor for Electronic Publications for the IEEE Control Systems Society and Associate Editor for the Journal of Discrete Event Systems and Nonlinear Analysis: Hybrid Systems. Magnus Egerstedt is the director of the Georgia Robotics and Intelligent Systems Laboratory (GRITS Lab), is a Fellow of the IEEE, received the ECE/GT Outstanding Junior Faculty Member Award in 2005, the Georgia Tech Teaching Efficiency Award in 2012, and the CAREER Award from the U.S. National Science Foundation in 2003.</p> <p> </p> <p>Other contributors - for Controls and Art, Amy LaViers and Magnus Egerstedt (Eds.):</p> <p> </p> <p>Frederico Augugliaro, ETH Zurich</p> <p>John Baillieul, Professor Boston University</p> <p>Rodrigo F. Cadiz, Pontificia Universidad Cato ́lica de Chile</p> <p>Luis Ignacio Reyes Castro, Massachusetts Institute of Technology</p> <p>Willa Chen, Princeton University</p> <p>Marco Colasso, Pontificia Universidad Cato ́lica de Chile</p> <p>Raffaello D'Andrea, Professor, ETH Zurich</p> <p>Katherine Fitch, Princeton University</p> <p>Andrew B. Godbehere, University of California - Berkeley</p> <p>Ken Goldberg, Professor, University of California - Berkeley</p> <p>Jason von Heinz Meyer, Center for Puppetry Arts, Atlanta, GA</p> <p>Kelsey Hochgraf, Princeton University</p> <p>Cristian Huepe, CHuepe Labs</p> <p>Elizabeth Jochum, Northwestern University</p> <p>Elliot Johnson, Northwestern University</p> <p>Peter Kingston, Georgia Institute of Technology</p> <p>Naomi Leonard, Edwin Wiley Professor, Princeton University</p> <p>Susan Marshall, Director, Professor of Dance, Princeton University</p> <p>Todd Murphey, Associate Professor, Northwestern University</p> <p>Kayhan Özcimder, Boston University</p> <p>Angela Schoellig, Assistant Professor, University of Toronto</p> <p>Jarvis Shultz, Northwestern University</p> <p>Hallie Siegel, ETH Zurich</p> <p>Daniel T. Swain, Princeton University</p> <p>Lori Teague, Director and Associate Professor, Emory University</p> <p>Aaron Trippe, Princeton University<br> Panagiotis Tsiotras, Professor, Georgia Institute of Technology</p> <p>George F. Young, Princeton University</p>

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