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8.5 Summary 123
a) b) c)
d)
Figure 8.11: The PSOM resolves redundancies by extra constraints in a conve-
nient functional definition. (a-c) Sequence of images, showing how the Puma
manipulator turns from a joint configuration close to the range limits (a) to a con-
figuration with a larger mobility reserve (c). The stroboscopic picture (d) demon-
strates that the same tool center point is kept.
8.5 Summary
The PSOM learning algorithm shows very good generalization capability
for smooth continuous mapping tasks. This property becomes highlighted
at the robot finger inverse kinematics problem with 3 inherent degrees-of-
freedom (see also 6 D kinematics). Since in many robotics learning tasks
the data set can be actively sampled, the PSOM's ability to construct the
high-dimensional manifold from a small number of training data turns out
to be here a many-sided beneficial mechanism for rapid learning.
