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Developmental Organization of Robot Behavior [Kõva köide]

  • Formaat: Hardback, 360 pages, kõrgus x laius: 229x178 mm, 60 colour illustrations., 7 black and white illustrations
  • Sari: Intelligent Robotics and Autonomous Agents series
  • Ilmumisaeg: 14-Mar-2023
  • Kirjastus: MIT Press
  • ISBN-10: 0262073005
  • ISBN-13: 9780262073004
Teised raamatud teemal:
Developmental Organization of Robot BehaviorSuurem pilt
  • Formaat: Hardback, 360 pages, kõrgus x laius: 229x178 mm, 60 colour illustrations., 7 black and white illustrations
  • Sari: Intelligent Robotics and Autonomous Agents series
  • Ilmumisaeg: 14-Mar-2023
  • Kirjastus: MIT Press
  • ISBN-10: 0262073005
  • ISBN-13: 9780262073004
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780262073004.html
Märksõnad:
"This book explores the question of how robots might "learn" how to behave in novel environments, without having each possible action pre-programmed"--

A comprehensive introduction to the mathematical foundations of movement and actuation that apply equally to animals and machines.

This textbook offers a computational framework for the sensorimotor stage of development as applied to robotics. Much work in developmental robotics is based on ad hoc examples, without a full computational basis. This book's comprehensive and complete treatment fills the gap, drawing on the principal mechanisms of development in the first year of life to introduce what is essentially an operating system for developing robots. The goal is to apply principles of development to robot systems that not only achieve new levels of performance but also provide evidence for scientific theories of human development.
Preface xv
Acknowledgments xxi
1 Introduction
1(16)
1.1 Knowledge and Representation
3(2)
1.2 Embodied Cognitive Systems
5(1)
1.3 Developmental Robotics 5 Example: Learning to Walk: A Developmental Conspiracy
6(2)
1.4 Frontiers in Robotics
8(2)
1.5 Organization of the Book
10(2)
1.6 Exercises
12(5)
I Motor Units
2 Actuation
17(30)
2.1 Muscle
17(7)
2.1.1 The Contractile Proteins
18(1)
2.1.2 The Sliding Filament Model
18(3)
2.1.3 Active and Passive Muscle Dynamics
21(3)
2.2 Robot Actuators
24(19)
2.2.1 Permanent Magnet DC Electric Motors
24(7)
Example: Torque-Speed Calculation
31(4)
2.2.2 Hydraulic Actuators
35(2)
2.2.3 Pneumatic Actuators
37(3)
2.2.4 Emerging Actuator Technologies
40(3)
2.3 Exercises
43(4)
3 Closed-Loop Control
47(32)
3.1 The Closed-Loop Spinal Stretch Reflex
48(3)
3.1.1 Spinal Processing
48(1)
3.1.2 Motor Nuclei
49(2)
3.2 The Canonical Spring-Mass-Damper
51(7)
3.2.1 Equation of Motion: The Harmonic Oscillator
53(1)
3.2.2 Stability: Lyapunov's Direct Method
54(2)
Example: Stability Analysis for the Spring-Mass-Damper
56(2)
3.3 Proportional-Derivative Feedback Control
58(12)
3.3.1 A Primer for Laplace Transforms
60(1)
3.3.2 Stability in the Time-Domain
61(1)
3.3.3 The Transfer Function, SISO Filters, and the Time-Domain Response
61(2)
Example: Closed-Loop Oculomotor Transfer Function
63(2)
3.3.4 The Performance of Proportional-Derivative Controllers
65(2)
Example: Controlling Eye Movements
67(3)
3.4 Exercises
70(6)
Part I Summary: Muscles, Motors, and Control
76(3)
II STRUCTURE IN KINODYNAMIC SYSTEMS
4 Kinematic Systems
79(40)
4.1 Terminology
79(1)
4.2 Spatial Tasks
80(4)
Example: Kinematic Description of Roger-the-Crab
81(3)
4.3 Homogeneous Transforms
84(4)
4.3.1 Translational Components
84(1)
4.3.2 Rotational Components
85(2)
4.3.3 Inverting the Homogeneous Transform
87(1)
4.4 Manipulator Kinematics
88(7)
4.4.1 Forward Kinematics
88(1)
Example: Forward Kinematics of the Planar 2R Manipulator
88(3)
4.4.2 Inverse Kinematics
91(1)
Example: Geometric Inverse Kinematic Solution for the Planar 2R Manipulator
92(3)
4.5 Kinematics of Stereo Reconstruction
95(3)
4.5.1 Pinhole Camera: Projective Geometry
95(1)
4.5.2 Binocular Localization: Forward Kinematics
96(1)
Example: Stereo Localization in the Plane
96(2)
4.6 Hand-Eye Kinematic Transformations
98(3)
4.7 Kinematic Conditioning
101(8)
4.7.1 Jacobian
101(1)
4.7.2 The Manipulator Jacobian
101(1)
Example: First-Order Velocity Control for the Planar 2R Manipulator
102(4)
Example: Velocity and Force Ellipsoids for Roger
106(1)
4.7.3 Stereo Localizability
107(1)
Example: Roger's Oculomotor Jacobian and /Stereo Localizability
107(2)
4.8 Kinematic Redundancy
109(3)
Example: Self-Motion Manifold
110(2)
4.9 Exercises
112(7)
5 Hands and Kinematic Grasp Analysis
119(28)
5.1 The Human Hand
119(3)
5.2 Kinematic Innovations in Robot Hands
122(7)
5.3 Mathematical Description of Multiple Contact Systems
129(13)
5.3.1 Screw Systems
129(1)
Example: Twist Constraints on Object Mobility in a Planar Grasp
129(2)
5.3.2 The Grasp Jacobian
131(2)
5.3.3 Contact Types
133(3)
5.3.4 The Generalized Grasp Jacobian
136(1)
Example: The Two-Contact Grasp Jacobian
137(2)
5.3.5 Grasp Performance: Form and Force Closure
139(2)
Example: Solving for Forces in Force Closure Grasps
141(1)
5.4 Exercises
142(5)
6 Dynamics of Articulated Systems
147(22)
6.1 Newton's Laws
147(1)
6.2 The Inertia Tensor
148(5)
Example: Rotational Moment of Inertia
150(1)
6.2.1 The Parallel Axis Theorem
151(1)
Example: Translating the Center of Rotation
152(1)
6.2.2 Rotating the Inertia Tensor
153(1)
6.3 The Computed Torque Equation
153(9)
Example: Dynamic Model of Roger's Eye
154(1)
Example: Dynamic Model of Roger's Arm
155(2)
6.3.1 Simulation
157(1)
6.3.2 Feedforward Control
157(1)
6.3.3 Analysis: The Dynamic Manipulability Ellipsoid
158(2)
Example: Gravity and Roger
160(2)
6.4 Exercises
162(3)
Part II Summary: The Kinodynamic Affordances of Embodied Systems
165(4)
III STRUCTURE IN SENSORY FEEDBACK
7 Stimuli and Sensation: Organs of Visual and Tactile Perception
169(22)
7.1 Light
170(8)
7.1.1 Image Formation
170(4)
7.1.2 The Evolution of the Human Eye
174(4)
7.1.3 Photosensitive Image Planes
178(1)
7.2 Touch
178(9)
7.2.1 Cutaneous Mechanoreceptors
179(2)
7.2.2 Robotic Tactile Sensing
181(6)
7.3 Exercises
187(4)
8 Signals, Signal Processing, and Information
191(30)
8.1 Sampling Continuous Signals
191(8)
Example: Spectral Properties of the Human Voice
192(4)
8.1.1 The Sampling Theorem
196(3)
8.2 Discrete Convolution Operators
199(8)
8.2.1 Spectral Filtering
201(2)
8.2.2 Frei and Chen Signal Decomposition Operators
203(2)
8.2.3 Noise, Differentiation, and Differential Geometry
205(1)
Example: Edge Sharpening
205(2)
8.3 Structure and Causality in Signals
207(9)
8.3.1 Gaussian Operators
208(1)
8.3.2 The Gaussian Pyramid: Blobs
209(3)
8.3.3 Multi-Scale Edges, Ridges, and Corners
212(4)
8.4 Exercises
216(2)
Part III Summary: Transducers, Signals, and Perceptual Structure
218(3)
IV SENSORIMOTOR DEVELOPMENT
9 Infant Neurodevelopmental Organization
221(30)
9.1 The Evolution of the Brain
221(2)
9.2 Hierarchy in the Neocortex
223(6)
9.3 Neurodevelopmental Organization
229(14)
9.3.1 Limbic Reflexes: Visceral, Vegetative, and Behavioral
230(2)
9.3.2 Spinal - and Brainstem-Mediated Reflexes
232(4)
9.3.3 Bridge Reflexes
236(2)
9.3.4 Postural Reflexes
238(2)
9.3.5 Maturational Processes
240(3)
9.4 Developmental and Functional Chronology in the First Year
243(3)
9.5 Sensory and Cognitive Milestones
246(3)
9.5.1 Sensory Performance
246(2)
9.5.2 Cognitive Development in the Sensorimotor Stage
248(1)
9.6 Exercises
249(2)
10 A Computational Framework for Experiments in Developmental Learning
251(30)
10.1 Parametric Closed-Loop Reflexes
252(16)
10.1.1 Potential Functions: Φ
252(3)
10.1.2 Closed-Loop Actions: Φ|στ
255(1)
10.1.3 A Taxonomy of Parametric Actions
256(1)
Example: Manipulability Reflex
257(3)
10.1.4 Co-Articulation: Multi-Objective Control
260(1)
Example: The Mechanics of Human Finger Movement
261(3)
10.1.5 States: γ(φ,φ)
264(1)
Example: Representing Grasp Dynamics
265(3)
10.2 A Multimodal Landscape of Attractors
268(10)
Example: Multi-Objective Visual Inspection Task
270(3)
10.2.1 Reinforcement Learning in a Landscape of Attractors
273(3)
10.2.2 Skills
276(2)
10.3 Exercises
278(3)
11 Case Study: Learning to Walk
281(18)
11.1 Thing: A Quadruped
281(2)
11.2 Controllers and Control Combinatorics
283(3)
11.3 Locomotion Controllers
286(3)
11.3.1 Aggregate State Representation
287(1)
Example: Logical Organization of Locomotor Skills
288(1)
11.4 Learning the ROTATE Skill
289(1)
11.5 The STEP Skill
290(2)
11.6 Hierarchical walk and NAVIGATE Skills
292(2)
11.7 Developmental Performance: Hierarchical Gross Motor Skills
294(4)
Part IV Summary: Foundations for Hierarchical Skills
298(1)
Appendix A Tools for Linear Analysis
299(32)
A.1 Linear Algebra
299(3)
A.2 Matrix Inverse
302(1)
A.3 Definiteness
303(1)
A.4 Hessian
304(1)
A.5 Matrix Norms
305(1)
A.6 Quadratic Forms
305(2)
Example: Plotting the Quadratic Form
306(1)
A.7 Singular Value Decomposition
307(2)
A.8 Scalar Condition Metrics for Linear Transforms
309(3)
A.8.1 Minimum Singular Value
309(1)
A.8.2 Condition Number
309(1)
A.8.3 Volume
310(1)
A.8.4 Radius
310(1)
Example: Scalar Conditioning Metrics Applied to Roger's Arm
311(1)
A.9 The Pseudoinverse
312(3)
A.10 Linear Integral Transforms
315(6)
A.10.1 Complex Numbers
316(1)
A.10.2 Fourier Transform
317(3)
A.10.3 Laplace Transform
320(1)
Example: Laplace Transform of an Exponential Function f(i)=e1
320(1)
Example: Laplace Transform of the Unit Step Function f(t) = t ≤ 0
321(1)
A.11 Time-Domain Responses for the Harmonic Oscillator
321(10)
Example: Time-Domain Response of the Spring-Mass-Damper
323(2)
Example: The Root Locus Diagram for the PD Control System
325(1)
A.11.1 Frequency-Dependent Amplitude and Phase Response
326(3)
A.11.2 Stiffness and Impedance
329(2)
Appendix B The Dynamics of Kinematic Chains
331(20)
B.1 Deriving the Inertia Tensor
331(3)
B.2 Inertial Coordinate Frames
334(1)
B.3 Rotating Coordinate Systems
334(3)
B.4 Newton-Euler Iterations
337(12)
B.4.1 Propagating Velocities in Open Kinematic Chains
338(2)
B.4.2 Propagating Force in Open Kinematic Chains
340(2)
B.4.3 The Outward-Inward Iteration
342(2)
Example: The Computed Torque Equation for the Planar 2R Manipulator
344(5)
B.5 Lagrangian Mechanics
349(2)
Appendix C Numerical Methods for Solving Laplace's Equation
351(6)
Example: A Collision-Free Arm Controller for Roger
353(4)
Bibliography 357(14)
Index 371