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Learning for Adaptive & Reactive Robot Control
by Billard, Mirrazavi
ISBN: 9780262367028 | Copyright 2022
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| Cover (pg. i) | |
| I Introduction (pg. 1) | |
| 1 Using and Learning Dynamical Systemsfor Robot Control—Overview (pg. 3) | |
| 1.1 Prerequisites and Additional Material (pg. 3) | |
| 1.2 Trajectory Planning under Uncertainty (pg. 4) | |
| 1.3 Computing Paths with DSs (pg. 9) | |
| 1.4 Learning a Control Law to Plan Paths Automatically (pg. 13) | |
| 1.5 Learning How to Combine Control Laws (pg. 14) | |
| 1.6 Modifying a Control Law through Learning (pg. 15) | |
| 1.7 Coupling DSs (pg. 18) | |
| 1.8 Generating and Learning Compliant Control with DSs (pg. 20) | |
| 1.9 Control Architectures (pg. 22) | |
| 2 Gathering Data for Learning (pg. 27) | |
| 2.1 Approaches to Generate Data (pg. 27) | |
| 2.2 Interfaces for Teaching Robots (pg. 29) | |
| 2.3 Desiderata for the Data (pg. 34) | |
| 2.4 Teaching a Robot How to Play Golf (pg. 36) | |
| 2.5 Gathering Data from Optimal Control (pg. 40) | |
| 3 Learning a Control Law (pg. 45) | |
| 3.1 Preliminaries (pg. 46) | |
| 3.2 Nonlinear DSs as a Mixture of Linear Systems (pg. 55) | |
| 3.3 Learning Stable, Nonlinear DSs (pg. 57) | |
| 3.4 Learning Stable, Highly Nonlinear DSs (pg. 76) | |
| 3.5 Learning Stable, Second-Order DSs (pg. 103) | |
| 3.6 Conclusion (pg. 109) | |
| II Learning a Controller (pg. 43) | |
| 4 Learning Multiple Control Laws (pg. 111) | |
| 4.1 Combining Control Laws through State-Space Partitioning (pg. 111) | |
| 4.2 Learning of DSs with Bifurcations (pg. 121) | |
| 5 Learning Sequences of Control Laws (pg. 131) | |
| 5.1 Learning Locally Active Globally Stable Dynamical Systems (pg. 133) | |
| 5.2 Learning Sequences of LPV-DS with Hidden Markov Models (pg. 154) | |
| III Coupling and Modulating Controllers (pg. 173) | |
| 6 Coupling and Synchronizing Controllers (pg. 175) | |
| 6.1 Preliminaries (pg. 176) | |
| 6.2 Coupling Two Linear DSs (pg. 177) | |
| 6.3 Coupling Arm-Hand Movement1 (pg. 180) | |
| 6.4 Coupling Eye-Hand-Arm Movements4 (pg. 189) | |
| 7 Reaching for and Adapting to Moving Objects (pg. 195) | |
| 7.1 How to Reach for a Moving Object (pg. 196) | |
| 7.2 Unimanual Reaching for a Fixed Small Object (pg. 198) | |
| 7.3 Unimanual Reaching for a Moving Small Object (pg. 202) | |
| 7.4 Robotic Implementation (pg. 205) | |
| 7.5 Bimanual Reaching for a Moving Large Object (pg. 209) | |
| 7.6 Robotic Implementation (pg. 213) | |
| 8 Adapting and Modulating an Existing Control Law (pg. 219) | |
| 8.1 Preliminaries (pg. 219) | |
| 8.2 Learning an Internal Modulation (pg. 223) | |
| 8.3 Learning an External Modulation4 (pg. 230) | |
| 8.4 Modulation to Transit from Free Space to Contact (pg. 236) | |
| 9 Obstacle Avoidance (pg. 245) | |
| 9.1 Obstacle Avoidance: Formalism (pg. 246) | |
| 9.2 Self-Collision, Joint-Level Obstacle Avoidance (pg. 257) | |
| IV Compliant and Force Control with Dynamical Systems (pg. 267) | |
| 10 Compliant Control (pg. 269) | |
| 10.1 When and Why Should a Robot Be Compliant? (pg. 269) | |
| 10.2 Compliant Motion Generators (pg. 273) | |
| 10.3 Learning the Desired Impedance Profiles (pg. 285) | |
| 10.4 Passive Interaction Control with DSs (pg. 287) | |
| 11 Force Control (pg. 295) | |
| 11.1 Motion and Force Generation in Contact Tasks with DSs (pg. 295) | |
| 12 Conclusion and Outlook (pg. 303) | |
| AppA-B (pg. 305) | |
| V Appendices (pg. 305) | |
| A Background on Dynamical Systems Theory (pg. 307) | |
| A.1 Dynamical Systems (pg. 307) | |
| A.2 Visualization of Dynamical Systems (pg. 308) | |
| A.3 Linear and Nonlinear Dynamical Systems (pg. 308) | |
| A.4 Stability Definitions (pg. 309) | |
| A.5 Stability Analysis and Lyapunov Stability (pg. 311) | |
| A.6 Energy Conservation and Passivity (pg. 312) | |
| A.7 Limit Cycles (pg. 313) | |
| A.8 Bifurcations (pg. 314) | |
| B Background on Machine Learning (pg. 315) | |
| B.1 Machine Learning Problems (pg. 315) | |
| B.2 Metrics (pg. 316) | |
| B.3 Gaussian Mixture Models (pg. 319) | |
| B.4 Support Vector Machines (pg. 337) | |
| B.5 Gaussian Processes Regression (pg. 348) | |
| AppC-D (pg. 357) | |
| C Background on Robot Control (pg. 357) | |
| C.1 Multi-rigid Body Dynamics (pg. 357) | |
| C.2 Motion Control (pg. 357) | |
| D Proofs and Derivations (pg. 361) | |
| D.1 Proofs and Derivations for Chapter 3 (pg. 361) | |
| D.2 Proofs and Derivations for Chapter 4 (pg. 362) | |
| D.3 Proofs and Derivations for Chapter 5 (pg. 363) | |
| D.4 Proofs and Derivations for Chapter 9 (pg. 373) | |
| Endnotes (pg. 379) | |
| Bibliography (pg. 383) | |
| Index (pg. 391) | |
| Contents (pg. vii) | |
| Preface (pg. xiii) | |
| Notation (pg. xix) | |
| I. Introduction (pg. 1) | |
| 1. Using and Learning Dynamical Systems for Robot Control—Overview (pg. 3) | |
| 1.1 Prerequisites and Additional Material (pg. 3) | |
| 1.2 Trajectory Planning under Uncertainty (pg. 4) | |
| 1.3 Computing Paths with DSs (pg. 9) | |
| 1.4 Learning a Control Law to Plan Paths Automatically (pg. 13) | |
| 1.5 Learning How to Combine Control Laws (pg. 14) | |
| 1.6 Modifying a Control Law through Learning (pg. 15) | |
| 1.7 Coupling DSs (pg. 18) | |
| 1.8 Generating and Learning Compliant Control with DSs (pg. 20) | |
| 1.9 Control Architectures (pg. 22) | |
| 2. Gathering Data for Learning (pg. 27) | |
| 2.1 Approaches to Generate Data (pg. 27) | |
| 2.2 Interfaces for Teaching Robots (pg. 29) | |
| 2.3 Desiderata for the Data (pg. 34) | |
| 2.4 Teaching a Robot How to Play Golf (pg. 36) | |
| 2.5 Gathering Data from Optimal Control (pg. 40) | |
| II. Learning a Controller (pg. 43) | |
| 3. Learning a Control Law (pg. 45) | |
| 3.1 Preliminaries (pg. 46) | |
| 3.2 Nonlinear DSs as a Mixture of Linear Systems (pg. 55) | |
| 3.3 Learning Stable, Nonlinear DSs (pg. 57) | |
| 3.4 Learning Stable, Highly Nonlinear DSs (pg. 76) | |
| 3.5 Learning Stable, Second-Order DSs (pg. 103) | |
| 3.6 Conclusion (pg. 109) | |
| 4. Learning Multiple Control Laws (pg. 111) | |
| 4.1 Combining Control Laws through State-Space Partitioning (pg. 111) | |
| 4.2 Learning of DSs with Bifurcations (pg. 121) | |
| 5. Learning Sequences of Control Laws (pg. 131) | |
| 5.1 Learning Locally Active Globally Stable Dynamical Systems (pg. 133) | |
| 5.2 Learning Sequences of LPV-DS with Hidden Markov Models (pg. 154) | |
| III. Coupling and Modulating Controllers (pg. 173) | |
| 6. Coupling and Synchronizing Controllers (pg. 175) | |
| 6.1 Preliminaries (pg. 176) | |
| 6.2 Coupling Two Linear DSs (pg. 177) | |
| 6.3 Coupling Arm-Hand Movement (pg. 180) | |
| 6.4 Coupling Eye-Hand-Arm Movements (pg. 189) | |
| 7. Reaching for and Adapting to Moving Objects (pg. 195) | |
| 7.1 How to Reach for a Moving Object (pg. 196) | |
| 7.2 Unimanual Reaching for a Fixed Small Object (pg. 198) | |
| 7.3 Unimanual Reaching for a Moving Small Object (pg. 202) | |
| 7.4 Robotic Implementation (pg. 205) | |
| 7.5 Bimanual Reaching for a Moving Large Object (pg. 209) | |
| 7.6 Robotic Implementation (pg. 213) | |
| 8. Adapting and Modulating an Existing Control Law (pg. 219) | |
| 8.1 Preliminaries (pg. 219) | |
| 8.2 Learning an Internal Modulation (pg. 223) | |
| 8.3 Learning an External Modulation (pg. 230) | |
| 8.4 Modulation to Transit from Free Space to Contact (pg. 236) | |
| 9. Obstacle Avoidance (pg. 245) | |
| 9.1 Obstacle Avoidance: Formalism (pg. 246) | |
| 9.2 Self-Collision, Joint-Level Obstacle Avoidance (pg. 257) | |
| IV. Compliant and Force Control with Dynamical Systems (pg. 267) | |
| 10. Compliant Control (pg. 269) | |
| 10.1 When and Why Should a Robot Be Compliant? (pg. 269) | |
| 10.2 Compliant Motion Generators (pg. 273) | |
| 10.3 Learning the Desired Impedance Profiles (pg. 285) | |
| 10.4 Passive Interaction Control with DSs (pg. 287) | |
| 11. Force Control (pg. 295) | |
| 11.1 Motion and Force Generation in Contact Tasks with DSs (pg. 295) | |
| 12. Conclusion and Outlook (pg. 303) | |
| V. Appendices (pg. 305) | |
| A. Background on Dynamical Systems Theory (pg. 307) | |
| A.1 Dynamical Systems (pg. 307) | |
| A.2 Visualization of Dynamical Systems (pg. 308) | |
| A.3 Linear and Nonlinear Dynamical Systems (pg. 308) | |
| A.4 Stability Definitions (pg. 309) | |
| A.5 Stability Analysis and Lyapunov Stability (pg. 311) | |
| A.6 Energy Conservation and Passivity (pg. 312) | |
| A.7 Limit Cycles (pg. 313) | |
| A.8 Bifurcations (pg. 314) | |
| B. Background on Machine Learning (pg. 315) | |
| B.1 Machine Learning Problems (pg. 315) | |
| B.2 Metrics (pg. 316) | |
| B.3 Gaussian Mixture Models (pg. 319) | |
| B.4 Support Vector Machines (pg. 337) | |
| B.5 Gaussian Processes Regression (pg. 348) | |
| C. Background on Robot Control (pg. 357) | |
| C.1 Multi-rigid Body Dynamics (pg. 357) | |
| C.2 Motion Control (pg. 357) | |
| D. Proofs and Derivations (pg. 361) | |
| D.1 Proofs and Derivations for Chapter 3 (pg. 361) | |
| D.2 Proofs and Derivations for Chapter 4 (pg. 362) | |
| D.3 Proofs and Derivations for Chapter 5 (pg. 363) | |
| D.4 Proofs and Derivations for Chapter 9 (pg. 373) | |
| Notes (pg. 379) | |
| Bibliography (pg. 383) | |
| Index (pg. 391) | |
Aude Billard
Aude Billard is Professor, School of Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) and Director of the Learning Algorithms and Systems Laboratory (LASA).
Sina Mirrazavi
Sina Mirrazavi is a Senior Researcher at Sony.
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