The specification and validation of robotics applications require bridging the gap between formulating requirements and systematic testing. This often involves manual and error-prone tasks that become more complex as requirements, design, and implementation evolve. To address this challenge systematically, we propose extending behaviour-driven development (BDD) to define and verify acceptance criteria for robotic systems. In this context, we use domain-specific modelling and represent composable BDD models as knowledge graphs for robust querying and manipulation, facilitating the generation of executable testing models. A domain-specific language helps to efficiently specify robotic acceptance criteria. We explore the potential for automated generation and execution of acceptance tests through a software architecture that integrates a BDD framework, Isaac Sim, and model transformations, focusing on acceptance criteria for pick-and-place applications. We tested this architecture with an existing pick-and-place implementation and evaluated the execution results, which shows how this application behaves and fails differently when tested against variations of the agent and environment. This research advances the rigorous and automated evaluation of robotic systems, contributing to their reliability and trustworthiness.

Testing and evaluation of robotics systems is a difficult and oftentimes tedious task due to the systems' complexity and a lack of tools to conduct reproducible robotics experiments. Additionally, almost all available tools are either tailored towards a specific application domain, simulator or middleware. Particularly scenario-based testing, a common practice in the domain of automated driving, is not sufficiently covered in the robotics domain. In this paper, we propose a novel backend- and middleware-agnostic approach for conducting systematic, reproducible and automatable robotics experiments called Scenario Execution for Robotics. Our approach is implemented as a Python library built on top of the generic scenario description language OpenSCENARIO 2 and Behavior Trees and is made publicly available on GitHub. In extensive experiments, we demonstrate that our approach supports multiple simulators as backend and can be used as a standalone Python-library or as part of the ROS2 ecosystem. Furthermore, we demonstrate how our approach enables testing over ranges of varying values. Finally, we show how Scenario Execution for Robotics allows to move from simulation-based to real-world experiments with minimal adaptations to the scenario description file.