A Development Cycle for Automated Self-Exploration of Robot Behaviors
This addresses the problem of automating robot development processes for designers and engineers, but it appears incremental as it combines existing techniques.
The paper tackles the complexity in designing robotic systems by introducing Q-Rock, a development cycle for automated self-exploration and qualification of robot behaviors, resulting in a proof-of-concept implementation and use case demonstration.
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.