Neuromorphic and Brain-Based Robots

Cover......Page 1
Neuromorphic and Brain-Based Robots......Page 2
Title......Page 4
Copyright......Page 5
Contents......Page 6
Contributors......Page 8
Preface......Page 12
Part I Introduction......Page 14
1 History and potential of neuromorphic robotics......Page 16
Part II Neuromorphic robots: biologically and neurally inspired designs......Page 22
2.1 Introduction......Page 24
2.2.1 Why human-like structure?......Page 25
2.2.3 Skin structure......Page 26
2.3 Stable recognition through repetitive grasping......Page 29
2.3.1 Repetitive grasping strategy......Page 30
2.3.2 Classifying objects based on haptic sensory information......Page 32
References......Page 34
3.1 Introduction......Page 36
3.2 The rat whisker system......Page 37
3.3.1 Morphology, sensory transduction, and whisker actuation......Page 38
3.3.2 Whisker motion and active sensing behavior......Page 39
3.3.3 Neurobiology of the rat vibrissal system......Page 42
3.4 Whiskerbot......Page 43
Whiskers and transducers......Page 44
Platform......Page 45
Computing......Page 46
Whisker pattern generation......Page 47
Sensory transduction through a model of the whisker follicle and trigeminal ganglion......Page 49
Action selection......Page 50
3.4.3 Whiskerbot experiments......Page 51
Signal transduction......Page 53
Microvibrissae......Page 54
Head and body design and actuation......Page 55
Using MIMC to control whisker spread......Page 56
Predicting and canceling sensory signals due to self-movement......Page 57
3.5.3 Scratchbot experiments......Page 58
3.6.1 Why build robot models of animals and their nervous systems?......Page 61
Testing the sufficiency of theories......Page 63
Investigating the role of embodiment......Page 64
Evaluating the usefulness of biological solutions to problems in robotics......Page 65
References......Page 66
4.1 Introduction......Page 71
4.1.1 Background......Page 72
4.2 Platform......Page 75
4.2.1 Intelligent algorithm programming interface......Page 80
4.3.1 Parallel computing for improved performance of mobile brain algorithms......Page 81
4.3.2 BrainBot as a brain-derived vision algorithm and hardware development platform......Page 84
4.4.1 Voodoo control......Page 89
4.4.2 Social cognition......Page 91
4.5.1 Future work......Page 94
References......Page 95
Part III Brain-based robots: architectures and approaches......Page 98
5.1 Introduction......Page 100
Cell types......Page 102
Connectivity of regions......Page 103
5.2.1 Pose cells......Page 104
5.2.2 Local view cells......Page 106
5.2.4 Experience map maintenance......Page 107
5.3 Study 1: Mapping a large environment......Page 108
5.3.1 Interface to RatSLAM......Page 109
5.3.2 Experience map......Page 110
5.4.1 Interface to RatSLAM......Page 111
5.4.2 Experience map performance......Page 112
5.4.3 Path planning performance......Page 113
5.5 Study 3: Grounding symbols in the map......Page 114
5.5.1 Language games for locations......Page 115
5.6 Discussion......Page 117
References......Page 119
6.1 Introduction......Page 122
6.2 Evolution of intrinsic reward......Page 124
6.2.1 Improvement of intrinsic reward parameters......Page 125
6.3 Constrained policy gradient reinforcement learning......Page 126
6.3.1 Gradient projection......Page 127
6.3.2 Estimate of policy gradients......Page 128
6.4 Cyber Rodent hardware......Page 130
6.5.1 Stochastic policy......Page 131
6.5.2 Reward functions......Page 133
6.6.1 Improvement of fitness values......Page 134
6.6.2 Obtained intrinsic rewards and fitness values......Page 135
6.6.3 Relation between the internal battery level and fitness values......Page 138
6.7 Conclusion......Page 139
References......Page 140
7.1 Introduction......Page 142
7.2.1 ISAC cognitive architecture......Page 143
7.3 Relational mapping system: cognitive models for learning affordance relations......Page 145
7.3.1 Internal rehearsal system......Page 146
7.3.3 Internal rehearsal to learn affordance relations......Page 147
Performance......Page 148
Experimental results 2: Internal rehearsal using affordance relations......Page 149
Experimental method......Page 151
Performance......Page 152
7.4 Working memory system......Page 155
7.4.1 The cognitive structure: the perceptual system and the working memory structure combined......Page 157
7.4.3 Learn percepts for motion......Page 158
7.4.4 Learning a landmark location......Page 160
7.4.5 Learning a vision-guided navigation task......Page 161
Acknowledgments......Page 165
References......Page 166
8.1.1 Developmental robotics......Page 169
8.1.2 Biological architectures......Page 171
Top-down attention......Page 172
8.1.3 Biological visuomotor systems......Page 174
8.2 Network overview......Page 175
8.3 Hierarchical receptive fields......Page 177
8.4 Cortical activation......Page 178
8.5 Neuron competition......Page 180
8.6 Weight adaptation......Page 181
8.7.1 Development of layer 1......Page 182
8.7.2 Recognition through attention......Page 183
8.7.4 Motor-specific features......Page 185
8.8 Conclusion......Page 186
8.9 Discussion......Page 187
References......Page 188
9 Brain-inspired robots for autistic training and care......Page 191
9.1.1 Action, imitation, and social behavior......Page 192
9.1.2 Simulation of grasping behavior by autistic and typical people......Page 195
Dynamic neural field model for generation of human-like movements......Page 196
Integration of robots’ visual and proprioceptive cues simulates grasping behavior......Page 197
Effects of cue delays on grasping......Page 199
Modeling grasping behaviors of autistic and typical people by a robot.......Page 200
9.2 Social skill enhancement with robot-mediated games......Page 202
9.3 Robotic platforms for grasping, imitation, and social interaction games......Page 203
9.3.1 The i-blocks – a multiagent platform of interactive blocks......Page 205
9.3.2 The e-puck mobile robot......Page 207
9.3.3 The humanoid robot, NAO......Page 208
9.4 Laban movement analysis for emotional dialog between robots and humans......Page 209
9.5.1 Training gross motor skills, imitation, and turn taking......Page 214
9.5.2 Understanding and imitation of emotional behaviors......Page 219
9.6 Discussion......Page 222
References......Page 223
Part IV Philosophical and theoretical considerations......Page 228
10.1 From hardware and software to kernels and envelopes......Page 230
10.2.1 The reunited body......Page 231
10.2.2 Stable kernels......Page 233
10.2.3 Fluid body envelopes......Page 237
10.3.1 Incorporation: a misunderstood process......Page 239
10.3.2 A kernel for active exploration: history of a construct......Page 241
10.3.3 Reinterpretation of developmental patterns......Page 243
10.4.1 Can we identify neural circuits corresponding to a kernel in the human brain?......Page 245
10.4.2 Tonic dopamine as a signal of expected prediction of error decrease......Page 248
10.4.3 Cortical microcircuits as both prediction and metaprediction systems......Page 250
10.5 Concluding remarks......Page 252
Appendix: a kernel for progress-driven exploration of sensorimotor envelopes......Page 253
References......Page 257
11.1 Introduction......Page 264
11.3 Cognitive developmental robotics as a constructivist approach......Page 265
11.3.1 A cognitive developmental map......Page 267
11.4 Is a paradigm shift possible?......Page 268
11.5.1 Rethinking physical embodiment......Page 269
Cranial nerve system......Page 270
Musculoskeletal system......Page 271
11.5.2 Mirror neuron system and infrastructure for social cognitive development......Page 272
11.6 Sociality developmental model by CDR......Page 275
11.6.1 Self/others undifferentiated state......Page 276
11.6.2 The beginning of discrimination between self and non-self......Page 277
11.6.3 The existence of explicit others......Page 278
11.6.4 Interaction between the self and a caregiver as the other......Page 279
11.6.5 Social development of self concept and MNS......Page 280
11.7 Discussion......Page 281
References......Page 282
12.1 Introduction......Page 287
12.1.1 Machine vs. life......Page 288
12.1.2 Are we intentional, or perceived?......Page 290
12.2.1 Conscious and unconscious memories in the brain......Page 291
12.2.2 Making a frame at a moment, and the neural basis......Page 293
12.2.3 Functional roles of neural oscillations in the brain......Page 297
12.3.1 The brain is a dynamical and multitiered system......Page 300
12.3.2 Online robotic platform – brain-oscillation-based dynamics......Page 301
12.4 Summary and perspectives......Page 308
Acknowledgments......Page 310
References......Page 311
13.1 Introduction......Page 316
13.2 Brain-based devices......Page 317
13.3 Features and mechanisms of consciousness......Page 320
13.4 Empirical investigations of consciousness......Page 322
13.5.1 Nervous system......Page 324
13.5.2 Phenotype......Page 325
13.6.1 Behavioral correlates......Page 326
13.6.2 Neural correlates......Page 327
13.7 Conclusion......Page 328
Acknowledgments......Page 329
References......Page 330
Part V Ethical considerations......Page 334
14.1 Introduction......Page 336
14.2 Definitions and general remarks......Page 337
14.3 Historical background: Asimov’s laws......Page 338
14.4 Military robots......Page 340
14.5 Healthcare robots......Page 343
14.6 Other social robots......Page 348
14.7 Resolving ethical dilemmas......Page 349
14.8 Conclusion......Page 354
References......Page 356
15.1.1 What does autonomy mean for robots?......Page 358
15.1.2 The ethics of Neo-Crusoe......Page 360
15.2.1 A physical world where autonomous robots are possible......Page 362
15.2.2 Humans in a physicalistic world......Page 364
15.3.1 Theory of mind matters......Page 366
15.3.2 ASD and membership in the moral community......Page 367
15.3.3 ASD and moral theories......Page 369
15.4.2 An ethical landscape in the future......Page 371
Notes......Page 373
References......Page 374
Index......Page 375