Preface	6
	Acknowledgements	7
	Contents	8
	1 Introduction	17
	1.1 Overview	17
	1.2 Collaboration Between One Human--Robot Pair	20
	1.3 Collaboration Between Human and Multiple Robots/Swarms	22
	References	24
	2 Robust Shared-Control for Rear-Wheel Drive Cars	30
	2.1 Introduction	30
	2.2 Problem Formulation, Definitions, and Assumptions	31
	2.3 Design of the Shared-Control Law with Measurements of Absolute Positions	34
	2.3.1 Design of the Feedback Controller	34
	2.3.2 Shared-Control Algorithm	37
	2.4 Disturbance Rejections	40
	2.5 Design of the Shared Control Without Measurements of Absolute Positions	43
	2.5.1 Design of the Feedback Controller	44
	2.5.2 Shared-Control Algorithm	46
	2.6 Case Studies	48
	2.6.1 Case I: Turning Without Absolute Positioning	48
	2.6.2 Case II: Driving on a Road with Parked Cars	51
	2.6.3 Case III: Emergency Breaking	51
	2.7 Conclusions	53
	References	53
	3 Baxter-On-Wheels (BOW): An Assistive Mobile Manipulator for Mobility Impaired Individuals	56
	3.1 Introduction	56
	3.2 System Description	59
	3.2.1 Experimental Platform: BOW	59
	3.2.2 System Kinematics	63
	3.3 Control Algorithm	64
	3.3.1 Baseline Shared-Control Algorithm	64
	3.3.2 Free-Space Mode and Contact Mode	66
	3.4 Application to the BOW	69
	3.4.1 User Interface	69
	3.4.2 Object Pick-Up and Placement Task	70
	3.4.3 Board Cleaning Task	73
	3.5 Conclusion	75
	References	76
	4 Switchings Between Trajectory Tracking and Force Minimization in Human--Robot Collaboration	79
	4.1 Introduction	79
	4.2 Dynamic Models	81
	4.2.1 Robot Model	82
	4.2.2 Human Arm Model	82
	4.2.3 Unified Model	84
	4.2.4 Trajectory Tracking	85
	4.3 Control Design	85
	4.3.1 Control Objective	85
	4.3.2 Selection of Cost Functions	86
	4.3.3 Optimal Control	87
	4.4 Simulations	89
	4.4.1 Simulation Settings	89
	4.4.2 Change of Weights	90
	4.4.3 Adaptation of Desired Trajectory	92
	4.5 Conclusions	93
	References	94
	5 Estimating Human Intention During a Human--Robot Cooperative Task Based on the Internal Force Model	96
	5.1 Introduction	96
	5.2 Internal Force Model	99
	5.2.1 Problem Formulation	99
	5.2.2 Existing Models	100
	5.2.3 Proposed Model	101
	5.2.4 Discussion	104
	5.3 Method	104
	5.3.1 Apparatus	105
	5.3.2 Procedure	105
	5.4 Results	107
	5.5 Validation of the Model	110
	5.6 Statistical Analysis of the Internal Force Features	112
	5.6.1 Initial Grasp Force Magnitude	113
	5.6.2 Final Grasp Force Magnitude	114
	5.6.3 Internal Force Energy	114
	5.6.4 Difference Between Initial and Final Grasp Forces	115
	5.6.5 Internal Force Variation	115
	5.6.6 Negotiation Force	116
	5.6.7 Negotiation Force Versus Object Velocity	117
	5.7 Proposed Cooperation Policy	118
	5.8 Conclusion	120
	References	121
	6 A Learning Algorithm to Select Consistent Reactions to Human Movements	123
	6.1 Introduction	123
	6.2 Background	125
	6.2.1 Expert-Based Learning	125
	6.2.2 Binary Learning Algorithms	126
	6.3 Analysis	127
	6.3.1 Performance	128
	6.3.2 Consistency	128
	6.3.3 Adaptiveness	130
	6.3.4 Tie Breaking	131
	6.4 Expanded Dual Expert Algorithm	132
	6.4.1 Performance Analysis	133
	6.4.2 Consistency and Adaptiveness	134
	6.5 Simulation	134
	6.5.1 Dual Expert Algorithm	134
	6.5.2 Expanded Dual Expert Algorithm	135
	6.6 Experiment	137
	6.6.1 Setup	138
	6.6.2 Results	139
	6.7 Conclusions	141
	References	141
	7 Assistive Optimal Control-on-Request with Application in Standing Balance Therapy and Reinforcement	143
	7.1 Introduction	143
	7.2 Assistive Control Synthesis	145
	7.2.1 Calculating a Schedule of Optimal Infinitesimal Actions	145
	7.2.2 Computing the Control Duration	150
	7.3 Human--Robot Interaction in Assisted Balance Therapy	151
	7.3.1 Related Work: Assist-as-Needed Techniques	152
	7.3.2 Interactive Simulation Study	153
	7.4 Human--Robot Communication in Posture Reinforcement: A Short Study	157
	7.5 Conclusion	160
	References	161
	8 Intelligent Human--Robot Interaction Systems Using Reinforcement Learning and Neural Networks	164
	8.1 Introduction	164
	8.2 HRI Control: Motivation and Structure Overview of the Proposed Approach	166
	8.3 Inner Robot-Specific Loop	167
	8.4 Outer Task-Specific Loop Control	173
	8.4.1 Task-Specific Outer Loop Control Method: An LQR Approach	173
	8.4.2 Learning Optimal Parameters of the Prescribed Impedance Model Using Integral Reinforcement Learning	177
	8.5 Simulation Results	178
	8.6 Conclusion	185
	References	185
	9 Regret-Based Allocation of Autonomy in Shared Visual Detection for Human--Robot Collaborative Assembly in Manufacturing	188
	9.1 Introduction	188
	9.2 The Hybrid Cell for Human--Robot Collaborative Assembly	190
	9.3 Detection Problem Formulation with Focus on the Selected Assembly Task	193
	9.3.1 Description of the Problem	193
	9.3.2 Problem Formulation	196
	9.4 Bayesian Sequential Decision-Making Algorithm for Allocation of Autonomy	197
	9.5 Inclusion of Regret in Bayesian Decision-Making Algorithm for Allocation of Autonomy	198
	9.6 Illustration of the Decision-Making Approach	201
	9.6.1 Illustration of the Optimal Bayesian Decision-Making Approach	201
	9.6.2 Illustration of the Regret-Based Modified Decision-Making Approach	204
	9.7 Implementation Scheme of the Regret-Based Bayesian Decision-Making Approach for the Assembly Task	204
	9.7.1 The Overall Scheme in a Flowchart	204
	9.7.2 Measurement of Sensing Probability and Observation Cost	207
	9.7.3 Measurement Method for Regret Intensity	208
	9.8 Experimental Evaluation of the Regret-Based Bayesian Decision-Making Approach	210
	9.8.1 Objective	210
	9.8.2 Hypothesis	210
	9.8.3 The Evaluation Criteria	211
	9.8.4 The Experiment Design	211
	9.8.5 Subjects	211
	9.8.6 The Experimental Procedures	212
	9.9 Evaluation Results and Analyses	212
	9.10 Conclusions and Future Innovations	214
	References	215
	10 Considering Human Behavior Uncertainty and Disagreements in Human--Robot Cooperative Manipulation	217
	10.1 Introduction	217
	10.2 Human--Robot Cooperative Manipulation	219
	10.2.1 Cooperative Manipulation	219
	10.2.2 Control Challenges in Physical Human--Robot Interaction	222
	10.2.3 Reactive Assistants	222
	10.2.4 Proactive Assistants	223
	10.3 Interaction Wrench Decomposition	225
	10.3.1 Nonuniform Wrench Decomposition Matrices	226
	10.3.2 Effective and Internal Wrenches	227
	10.3.3 Load Share and Disagreement	231
	10.4 Optimal Robot Assistance Considering Human Behavior Uncertainty and Disagreements	231
	10.4.1 Anticipatory Assistance Based on Learned Models	232
	10.4.2 The Two-Dimensional Translational Case	237
	10.4.3 Experiments	239
	10.5 Conclusions	245
	References	248
	11 Designing the Robot Behavior for Safe Human--Robot Interactions	251
	11.1 Introduction	251
	11.1.1 The Safety Issues and Existing Solutions	252
	11.1.2 Safety Problems in HRI: Conflicts in Multiagent Systems	252
	11.1.3 Safe Control and Exploration	253
	11.2 Modeling the Human--Robot Interactions	254
	11.2.1 The Agent Model	254
	11.2.2 The Closed-Loop System	255
	11.2.3 Information Structure	256
	11.3 The Safety-Oriented Behavior Design	257
	11.3.1 The Safety Principle	257
	11.3.2 The Safety Index	258
	11.4 The Safe Set Algorithm (SSA)	260
	11.4.1 The Control Algorithm	261
	11.4.2 Online Learning and Prediction of Humans' Dynamics	262
	11.4.3 Applications	263
	11.5 The Safe Exploration Algorithm (SEA)	265
	11.5.1 The Safe Set in the Belief Space	266
	11.5.2 Learning in the Belief Space	267
	11.5.3 A Comparative Study Between SSA and SEA	270
	11.6 Combining SSA and SEA in Time Varying MAS Topology	273
	11.6.1 The Control Algorithm	274
	11.6.2 The Learning Algorithm	275
	11.6.3 Performance	275
	11.7 Discussions	276
	11.7.1 The Energy Based Methods	277
	11.7.2 Limitations and Future Work	277
	11.8 Conclusion	278
	References	278
	12 When Human Visual Performance Is Imperfect---How to Optimize the Collaboration Between One Human Operator and Multiple Field Robots	281
	12.1 Introduction	281
	12.2 Human and Robot Performance in Target Classification [4]	283
	12.3 Optimizing Human--Robot Collaboration for Target Classification	285
	12.3.1 Predetermined Site Allocation	285
	12.3.2 Optimized Site Allocation	290
	12.4 Numerical Results	293
	12.4.1 Collaboration Between the Human Operator and One Robot [14]	294
	12.4.2 Predetermined Site Allocation	296
	12.4.3 Optimized Site Allocation	303
	12.5 Conclusions	308
	References	308
	13 Human-Collaborative Schemes in the Motion Control of Single and Multiple Mobile Robots	310
	13.1 Introduction	310
	13.2 Modeling of the Robot and the Interactions	311
	13.2.1 Mobile Robot	311
	13.2.2 Communication Infrastructure	313
	13.2.3 Human--Robot Interface	313
	13.3 A Taxonomy of Collaborative Human--Robot Control	315
	13.3.1 Physical Domain of the Robots	315
	13.3.2 Degree of Autonomy from the Human Operator	316
	13.3.3 Force Interaction with the Operator	319
	13.3.4 Near-Operation Versus Teleoperation	321
	13.3.5 Physical Interaction with the Environment	322
	13.3.6 Use of Onboard Sensors Only	324
	13.4 A Taxonomy of Collaborative Human--Multi-robot Control	325
	13.4.1 Level of Centralization	325
	13.4.2 Master--Leader--Followers Schemes	326
	13.4.3 Formation-Orthogonal Control Schemes	327
	13.4.4 Group-Property Preservation Schemes	328
	13.4.5 Physical Interaction with Contact	329
	13.5 Conclusions	330
	References	331
	14 A Passivity-Based Approach to Human--Swarm Collaboration and Passivity Analysis of Human Operators	334
	14.1 Introduction	334
	14.2 Intended Scenario and Control Goals	337
	14.3 Control Architecture and Passivity	340
	14.4 Convergence Analysis	342
	14.4.1 Synchronization in Position Control Mode	342
	14.4.2 Synchronization in Velocity Control Mode	346
	14.5 Passivity of the Human Operator Decision Process	349
	14.5.1 Experimental Setup and Approach	350
	14.5.2 Analysis on Human Passivity in Position Control Mode	353
	14.5.3 Analysis on Human Passivity in Velocity Control Mode	356
	14.5.4 Analysis on Individual Variability	358
	14.6 Summary	362
	References	363
	15 Human--Swarm Interactions via Coverage of Time-Varying Densities	365
	15.1 Introduction	365
	15.2 Human--Swarm Interactions via Coverage	367
	15.2.1 The Coverage Problem	370
	15.2.2 Centralized Coverage of Time-Varying Densities	373
	15.2.3 Distributed Coverage of Time-Varying Densities	377
	15.3 Designing Density Functions	382
	15.3.1 Diffusion of Drawn Geometric Configurations	382
	15.3.2 Control of Gaussian Functions	384
	15.4 Robotic Experiments	385
	15.5 Conclusions	388
	References	389
	16 Co-design of Control and Scheduling for Human--Swarm Collaboration Systems Based on Mutual Trust	394
	16.1 Introduction	394
	16.2 Swarm Setup	397
	16.2.1 Dynamic Timing Model and Collaboration Delay	397
	16.2.2 Cooperative Control for Swarm Agents	399
	16.3 Collaboration Framework	405
	16.3.1 Trust Model	405
	16.3.2 Human Performance Model	406
	16.3.3 Swarm Performance Model	407
	16.3.4 Human Attention Preference	407
	16.3.5 Fitness	409
	16.4 Real-Time Scheduling	410
	16.5 Simulation Results	411
	16.5.1 Parameter Setup	411
	16.5.2 Results and Discussions	413
	16.6 Conclusions	417
	References	418
	Index	421