RobotValues：人类价值观冲突下的家庭机器人评估

摘要

尽管家用机器人通常基于任务完成情况进行评估，但日常家庭环境中常出现价值冲突的情境，此时机器人应选择优先考虑任务成功之外的其他价值（如人类自主性、效率或社会适宜性）的行动。然而，目前尚无针对此类场景中机器人价值偏好的评估基准。我们提出RobotValues——一个用于在1万种价值冲突场景中评估家用机器人规划器的基准。每个实例包含一张逼真的家庭环境图像，以及多个体现不同人类价值优先级的合理机器人行动。我们通过大语言模型辅助的场景生成、基于利益相关者的价值提取、图像生成及自动质量控制构建了RobotValues。利用RobotValues评估机器人领域使用的视觉-语言模型后，我们发现模型表现出默认价值偏好（包括安全性和适应性），但较少选择优先考虑隐私的行动。当要求模型优先处理与其自身偏好冲突的特定价值时，它们往往无法覆盖默认行动，在80%的情况下选择了错误行动。这些发现表明，家用机器人评估不仅应衡量任务完成或安全合规性，还应评估机器人在人类价值冲突时能否从合理行动中做出选择。

English

While household robots are often evaluated based on task completion, everyday domestic environments involve value-conflicting situations in which robots are expected to choose actions that prioritize other values than task success, such as human autonomy, efficiency, or social appropriateness. Yet, there are no benchmarks for evaluating robots' value preferences in such scenarios. We introduce RobotValues, a benchmark to evaluate household robot planners in 10K value-conflict scenarios. Each instance consists of a realistic household image with multiple plausible robot actions that prioritize different human values. We construct RobotValues through LLM-assisted scenario generation, stakeholder-grounded value extraction, image generation and automatic quality control. Using RobotValues we evaluate VLMs used in robotics and find that models exhibit default value preferences, including safety and accommodation, while underselecting privacy-prioritizing actions. When the models are instructed to prioritize specific values that conflict with their own preferences, they often fail to override their default actions, choosing incorrect actions for 80% of the time. These findings suggest that household robot evaluation should measure not only task completion or safety compliance, but also whether robots can choose among plausible actions when human values conflict.