Page 13 - Rapid Learning in Robotics

LIST OF FIGURES xi

8.3 [a–b] Mapping accuracy of the inverse finger kinematics
problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
8.4 [a–b] The robot finger training data for the MLP networks . 112
8.5 [a–c] The training data for the PSOM networks. . . . . . . . 113
8.6 The six Puma axes . . . . . . . . . . . . . . . . . . . . . . . . . 114
8.7 Spatial accuracy of the 6 DOF inverse robot kinematics . . . 116
8.8 PSOM adaptability to sudden changes in geometry . . . . . 118
8.9 Modulating the cost function: “discomfort” example . . . . . 121
8.10 [a–d] Intermediate steps in optimizing the mobility reserve 121
8.11 [a–d] The PSOM resolves redundancies by extra constraints 123

9.1 Context dependent mapping tasks . . . . . . . . . . . . . . . 126
9.2 The investment learning phase . . . . . . . . . . . . . . . . . . 127
9.3 The one-shot adaptation phase . . . . . . . . . . . . . . . . . . . 128
9.4 [a–b] The “mixture-of-experts” versus the “mixture-of-expertise”
architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
9.5 [a–c] Three variants of the “mixture-of-expertise” architecture131
9.6 [a–b] 2 D visuo-motor coordination . . . . . . . . . . . . . . 133
9.7 [a–b] 3 D visuo-motor coordination with stereo vision . . . . 136

(10/207) Illustrations contributed by Dirk Selle [2.5], Ján Jockusch [2.8,
2.9], and Bernd Fritzke [6.8].