A development cycle for automated self-exploration of robot behaviors
Open Access
- 5 July 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in AI Perspectives
- Vol. 3 (1), 1-29
- https://doi.org/10.1186/s42467-021-00008-9
Abstract
In this paper we introduce Q-Rock, a development cycle for the automated self-exploration and qualification of robot behaviors. WithQ-Rock, we suggest a novel, integrative approach to automate robot development processes.Q-Rockcombines several machine learning and reasoning techniques to deal with the increasing complexity in the design of robotic systems. TheQ-Rockdevelopment 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 formalizeQ-Rock’s integrative development cycle and highlight its benefits with a proof-of-concept implementation and a use case demonstration.Keywords
Funding Information
- Bundesministerium für Bildung und Forschung (01IW18003)
- Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI)
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