Page 36 - Rapid Learning in Robotics

22 The Robotics Laboratory

However, the tremendous growth in general-purpose computing power
allows to shift already the entire exploratory phase of vision algorithm
development to general-purpose high-bandwidth computers. Fig. 2.2 ex-
poses various graphic workstations and high-bandwidth server machines
at the LAN network.

2.5 Concluding Remarks

We described work invested for establishing a versatile robotics hardware
infrastructure (for a more extended description see Walter and Ritter 1996c).
It is a testbed to explore, develop, and evaluate ideas and concepts. This
investment was also prerequisite of a variety of other projects, e.g. (Littmann
et al. 1992; Kummert et al. 1993a; Kummert et al. 1993b; Wengerek 1995;
Littmann et al. 1996).
An experimental robot system comprises many different components,
each exhibiting its own characteristics. The integration of these sub-systems
requires quite a bit of effort. Not many components are designed as intel-
ligent, open sub-systems, rather than systems by themselves.
Our experience shows, that good design of re-usable building blocks
with suitably standardized software interfaces is a great challenge. We
find it a practical need in order to achieve rapid experimentation and eco-
nomical re-use. An important issue is the sharing and interoperating of
robotics resources via electronic networks. Here the hardware architec-
ture must be complemented by a software framework, which complies to
the special needs of a complex, distributed robotics hardware. Efforts to
tackle this problem are beyond the scope of the present work and therefore
described elsewhere (Walter and Ritter 1996e; Walter 1996).
In practice, the time for gathering training data is a significant issue.
It includes also the time for preparing the learning set-up, as well as the
training phase. Working with robots in reality clearly exhibits the need
for those learning algorithms, which work efficiently also with a small
number of training examples.