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.
@article{pub11622,
author = {
Roehr, Thomas M.
and
Harnack, Daniel
and
Wöhrle, Hendrik
and
Wiebe, Felix
and
Schilling, Moritz
and
Lima, Oscar
and
Langosz, Malte
and
Kumar, Shivesh
and
Straube, Sirko
and
Kirchner, Frank
},
title = {A Development Cycle for Automated Self-Exploration of Robot Behaviors},
year = {2021},
month = {7},
volume = {3},
number = {1},
journal = {AI Perspectives},
publisher = {n.n.}
}
German Research Center for Artificial Intelligence Deutsches Forschungszentrum für Künstliche Intelligenz