用视觉语言模型生成人类异常行为，测试工业移动机器人安全边界。
Vision Language Model-based Testing of Industrial Autonomous Mobile Robots
- 用VLM根据需求自动生成违反安全规则的人类交互行为。
- 相比基线方法，生成场景更易触发机器人异常反应，提升测试覆盖度。
- 适合机器人安全测试团队、自动驾驶研发人员使用。
西班牙PAL Robotics公司开发多种自主移动机器人（AMRs），部署于仓库、零售空间和办公室等复杂环境，与人类协同工作。由于人类行为不可预测，且机器人可能未训练应对所有未知情境，需在广泛的人机交互下测试其安全性。然而，真实环境中使用实际机器人和人类进行测试成本高、不现实且存在安全隐患（如导致人身伤害）。为此，我们提出一种基于视觉语言模型（VLM）的测试方法（RVSG），与PAL Robotics合作开发。基于功能与安全需求，RVSG利用VLM生成违反这些需求的人类行为。我们在模拟器中对最新款PAL Robotics AMR进行了评估，测试了多个需求和导航路径。结果表明，相比基线方法，RVSG能有效生成违规场景；同时，生成的场景显著增加了机器人行为的多样性，有助于揭示其不确定性表现。
原文摘要 · Abstract (English)
PAL Robotics, in Spain, builds a variety of Autonomous Mobile Robots (AMRs), which are deployed in diverse environments (e.g., warehouses, retail spaces, and offices), where they work alongside humans. Given that human behavior can be unpredictable and that AMRs may not have been trained to handle all possible unknown and uncertain behaviors, it is important to test AMRs under a wide range of human interactions to ensure their safe behavior. Moreover, testing in real environments with actual AMRs and humans is often costly, impractical, and potentially hazardous (e.g., it could result in human injury). To this end, we propose a Vision Language Model (VLM)-based testing approach (RVSG) for industrial AMRs developed together with PAL Robotics. Based on the functional and safety requirements, RVSG uses the VLM to generate diverse human behaviors that violate these requirements. We evaluated RVSG with several requirements and navigation routes in a simulator using the latest AMR from PAL Robotics. Our results show that, compared with the baseline, RVSG can effectively generate requirement-violating scenarios. Moreover, RVSG-generated scenarios increase variability in robot behavior, thereby helping reveal their uncertain behaviors.
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