A development cycle for automated self-exploration of robot behaviors

AI Perspectives

Oscar Lima¹  Moritz Schilling¹  Felix Wiebe¹  Malte Langosz¹  Thomas M. Roehr¹  Sirko Straube¹  Daniel Harnack¹  Shivesh Kumar¹  Frank Kirchner²  Hendrik Wöhrle³ 

[1] DFKI GmbH Robotics Innovation Center, Bremen, Germany;DFKI GmbH Robotics Innovation Center, Bremen, Germany;AG Robotics, Department of Mathematics and Computer Science, University of Bremen, Bremen, Germany;DFKI GmbH Robotics Innovation Center, Bremen, Germany;Institute for Communication Technology, Department of Information Technology, Dortmund University of Applied Sciences and Arts, Dortmund, Germany;

关键词: Robotics;    Self-exploration;    Robot behaviors;    Semantic annotation;    Development cycle;    Knowledge representation;   

DOI  :  10.1186/s42467-021-00008-9

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY