A Development Cycle for Automated Self-Exploration of Robot Behaviors

TL;DR

This paper presents Q-Rock, an innovative development cycle that automates robot behavior exploration, classification, and mapping to streamline robotic system design using machine learning and reasoning techniques.

Contribution

Introduction of Q-Rock, a novel, integrative development cycle combining machine learning and reasoning for automated robot behavior exploration and qualification.

Findings

Demonstrated Q-Rock's effectiveness with a proof-of-concept implementation.

Showcased improved collaboration among robot designers.

Validated the cycle's ability to handle complex robotic systems.

Abstract

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.