Preface |
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ix | |
Author Biography |
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xiii | |
Acknowledgements |
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xv | |
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1 | (16) |
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1.1 Overview of sEMG-based stiffness transfer |
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1 | (2) |
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1.2 Overview of robot learning motion skills from humans |
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3 | (3) |
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1.3 Overview of robot intelligent control design |
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6 | (2) |
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8 | (9) |
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Chapter 2 Robot Platforms and Software Systems |
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17 | (12) |
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17 | (1) |
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18 | (1) |
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19 | (1) |
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20 | (1) |
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20 | (1) |
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21 | (1) |
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22 | (1) |
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2.8 MATLAB Robotics Toolbox |
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23 | (2) |
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25 | (1) |
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26 | (1) |
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27 | (2) |
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Chapter 3 Human-Robot Stiffness Transfer-Based on sEMG Signals |
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29 | (32) |
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29 | (1) |
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3.2 Brief introduction of sEMG signals |
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30 | (1) |
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3.3 Calculation of human arm Jacobian matrix |
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30 | (2) |
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32 | (3) |
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3.4.1 Incremental stiffness estimation method |
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32 | (1) |
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3.4.2 Stochastic perturbation method |
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33 | (2) |
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3.5 Interface design for stiffness transfer |
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35 | (2) |
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3.6 Human-robot stiffness mapping |
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37 | (2) |
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3.7 Stiffness transfer for various tasks |
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39 | (18) |
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3.7.1 Comparative tests for lifting tasks |
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39 | (4) |
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43 | (2) |
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3.7.3 Human-robot-human writing skill transfer |
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45 | (7) |
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52 | (5) |
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57 | (1) |
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57 | (4) |
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Chapter 4 Learning and Generalization of Variable Impedance Skills |
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61 | (30) |
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61 | (1) |
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4.2 Overview of the framework |
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62 | (1) |
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4.3 Trajectory segmentation |
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63 | (4) |
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4.3.1 Data segmentation using difference method |
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63 | (1) |
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4.3.2 Beta process autoregressive hidden Markov model |
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64 | (3) |
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4.4 Trajectory alignment methods |
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67 | (1) |
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4.5 Dynamical movement primitives |
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67 | (1) |
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4.6 Modeling of impedance skills |
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68 | (2) |
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70 | (17) |
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4.7.1 Learning writing tasks |
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70 | (1) |
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71 | (3) |
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4.7.3 Cutting and lift-place tasks |
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74 | (8) |
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4.7.4 Water-lifting tasks |
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82 | (5) |
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87 | (1) |
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88 | (3) |
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Chapter 5 Learning Human Skills from Multimodal Demonstration |
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91 | (22) |
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91 | (1) |
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92 | (2) |
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5.3 HSMM-GMR Model Description |
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94 | (2) |
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5.3.1 Data Modeling with HSMM |
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94 | (1) |
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5.3.2 Task Reproduction with GMR |
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95 | (1) |
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5.4 Impedance Controller in Task Space |
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96 | (2) |
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98 | (11) |
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5.5.1 Button-pressing Task |
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98 | (2) |
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100 | (1) |
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101 | (7) |
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5.5.4 Experimental Analysis |
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108 | (1) |
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109 | (1) |
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110 | (3) |
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Chapter 6 Skill Modeling Based on Extreme Learning Machine |
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113 | (30) |
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113 | (1) |
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6.2 System of teleoperation-based robotic learning |
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114 | (7) |
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6.2.1 Overview of teleoperation demonstration system |
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114 | (1) |
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6.2.2 Motion Capture Approach based on Kinect |
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115 | (5) |
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6.2.3 Measurement of angular velocity by MYO armband |
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120 | (1) |
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6.2.4 Communication between Kinect and V-REP |
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120 | (1) |
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6.3 Human/robot joint angle calculation using Kinect camera |
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121 | (2) |
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6.4 Processing of demonstration data |
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123 | (3) |
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6.4.1 Dynamic time warping |
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123 | (2) |
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125 | (1) |
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6.4.3 Dragon naturally speaking system for verbal command |
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126 | (1) |
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6.5 Skill modeling using extreme learning machine |
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126 | (3) |
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129 | (11) |
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6.6.1 Motion Capture for Tracking of Human Arm Pose |
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129 | (1) |
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6.6.2 Teleoperation-Based demonstration in VREP |
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130 | (2) |
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6.6.3 VR-based teleoperation for task demonstration |
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132 | (2) |
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134 | (6) |
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140 | (1) |
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140 | (3) |
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Chapter 7 Neural Network-Enhanced Robot Manipulator Control |
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143 | (22) |
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143 | (1) |
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144 | (1) |
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7.3 Learning from multiple demonstrations |
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145 | (3) |
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7.3.1 Gaussian mixture model |
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146 | (1) |
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7.3.2 Fuzzy Gaussian mixture model |
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146 | (2) |
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7.4 Neural networks techniques |
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148 | (1) |
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7.4.1 Radial basis function neural network |
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148 | (1) |
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7.4.2 Cerebellar model articulation neural networks |
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149 | (1) |
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7.5 Robot manipulator controller design |
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149 | (6) |
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7.5.1 NN-based controller for robotic manipulator |
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149 | (5) |
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7.5.2 Adaptive admittance controller |
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154 | (1) |
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155 | (7) |
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7.6.1 Test of the adaptive admittance controller |
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155 | (3) |
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7.6.2 Test of the NN-based controller |
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158 | (1) |
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158 | (4) |
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162 | (1) |
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162 | (3) |
Index |
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165 | |