用行为驱动开发自动测试机器人系统，提升可靠性。

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.