Page 11 - Rapid Learning in Robotics

List of Figures

2.1 The Puma robot manipulator . . . . . . . . . . . . . . . . . . 10
2.2 The asymmetric multiprocessing “road map” . . . . . . . . . 11
2.3 The Puma force and position control scheme . . . . . . . . . 13
2.4 [a–b] The endeffector with “camera-in-hand” . . . . . . . . 15

2.5 The kinematics of the TUM robot fingers ... .. ... ... 16
2.6 The TUM hand hydraulic oil system . . . . . . . . . . . . . . 17
2.7 The hand control scheme . . . . . . . . . . . . . . . . . . . . . 18
2.8 [a–d] The sandwich structure of the multi-layer tactile sen-
sor .. ... ... ... .. ... ... ... ... .. ... ... 19
2.9 Tactile sensor system, simultaneous recordings . ... ... 20

3.1 [a–b] McCulloch-Pitts Neuron and the MLP network . . . . 24
3.2 [a–f] RBF network mapping properties . . . . . . . . . . . . 33
3.3 Distance versus topological distance . . . . . . . . . . . . . . 34
3.4 [a–b] The effect of over-fitting . . . . . . . . . . . . . . . . . . 36
3.5 The “Self-Organizing Map” (SOM) . . . . . . . . . . . . . . . 39

4.1 The “Parameterized Self-Organizing Map” (PSOM) . . . . . 44
4.2 [a–b] The continuous manifold in the embedding and the
parameter space . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.3 [a–c] 3 of 9 basis functions for a PSOM . . . . . . . . . . 46
4.4 [a–c] Multi-way mapping of the“continuous associative mem-
ory” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.5 [a–d] PSOM associative completion or recall procedure . . . 49
4.6 [a–d] PSOM associative completion procedure, reversed di-
rection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.7 [a–d] example unit sphere surface . . . . . . . . . . . . . . . 50
4.8 PSOM learning from scratch . . . . . . . . . . . . . . . . . . . 54
4.9 The modified adaptation rule Eq. 4.15 . . . . . . . . . . . . . 56

J. Walter “Rapid Learning in Robotics” ix