  In this paper we introduce Q-Rock, a development cycle for the automated
self-exploration and qualification of robot behaviors. With Q-Rock, we suggest
a novel, integrative approach to automate robot development processes. Q-Rock
combines several machine learning and reasoning techniques to deal with the
increasing complexity in the design of robotic systems. The Q-Rock development
cycle consists of three complementary processes: (1) automated exploration of
capabilities that a given robotic hardware provides, (2) classification and
semantic annotation of these capabilities to generate more complex behaviors,
and (3) mapping between application requirements and available behaviors. These
processes are based on a graph-based representation of a robot's structure,
including hardware and software components. A central, scalable knowledge base
enables collaboration of robot designers including mechanical, electrical and
systems engineers, software developers and machine learning experts. In this
paper we formalize Q-Rock's integrative development cycle and highlight its
benefits with a proof-of-concept implementation and a use case demonstration.
