Page 138 - Rapid Learning in Robotics

124 Application Examples in the Robotics Domain

Furthermore, the associative mapping concept has several interesting
properties. Several coordinate spaces can be maintained and learned si-
multaneously, as shown for the robot finger example. This multi-way
mapping solves, e.g. the forward and inverse kinematics with the very
same network. This simplifies learning and avoids any asymmetry of sep-
arate learning modules. As pointed out by Kawato (1995), the learning of
bi-directional mappings is not only useful for the planning phase (action
simulation), but also for bi-directional sensor–motor integrated control.
By the method of dynamic cost function modulation the PSOM's inter-
nal best-match search can be employed for partially meeting additional,
possibly conflicting target functions. This scheme was demonstrated in
the redundancy problem of the 6 DOF inverse robot kinematics.