Page 3 - Rapid Learning in Robotics

Jörg A. Walter

Rapid Learning in Robotics

Robotics deals with the control of actuators using various types of sensors
and control schemes. The availability of precise sensorimotor mappings
– able to transform between various involved motor, joint, sensor, and
physical spaces – is a crucial issue. These mappings are often highly non-
linear and sometimes hard to derive analytically. Consequently, there is a
strong need for rapid learning algorithms which take into account that the
acquisition of training data is often a costly operation.

The present book discusses many of the issues that are important to make
learning approaches in robotics more feasible. Basis for the major part of
the discussion is a new learning algorithm, the Parameterized Self-Organizing
Maps, that is derived from a model of neural self-organization. A key
feature of the new method is the rapid construction of even highly non-
linear variable relations from rather modestly-sized training data sets by
exploiting topology information that is not utilized in more traditional ap-
proaches. In addition, the author shows how this approach can be used in
a modular fashion, leading to a learning architecture for the acquisition of
basic skills during an “investment learning” phase, and, subsequently, for
their rapid combination to adapt to new situational contexts.