Page 21 - Rapid Learning in Robotics

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the cost of gathering the training data is very relevant as well as the avail-
ability of adaptable, high-dimensional sensorimotor transformations.
Chapter 7 and 8 present several PSOM examples in the vision and the
robotics domain. The flexible association mechanism facilitates applica-
tions: feature completion; dynamical sensor fusion, improving noise re-
jection; generating perceptual hypotheses for other sensor systems; vari-
ous robot kinematic transformation can be directly augmented to combine
e.g. visual coordinate spaces. This even works with redundant degrees of
freedom, which can additionally comply to extra constraints.
Chapter 9 turns to the next higher level of one-shot learning. Here the
learning of prototypical mappings is used to rapidly adapt a learning sys-
tem to new context situations. This leads to a hierarchical architecture,
which is conceptually linked, but not restricted to the PSOM approach.
One learning module learns the context-dependent skill and encodes
the obtained expertise in a (more-or-less large) set of parameters or weights.
A second meta-mapping module learns the association between the rec-
ognized context stimuli and the corresponding mapping expertise. The
learning of a set of prototypical mappings may be called an investment
learning stage, since effort is invested, to train the system for the second,
the one-shot learning phase. Observing the context, the system can now
adapt most rapidly by “mixing” the expertise previously obtained. This
mixture-of-expertise architecture complements the mixture-of-experts archi-
tecture (as coined by Jordan) and appears advantageous in cases where
the variation of the underlying model are continuous within the chosen
mapping domain.
Chapter 10 summarizes the main points.
Of course the full complexity of learning and the complexity of real robots
is still unsolved today. The present work attempts to make a contribution
to a few of the many things that still can be and must be improved.