RobotValues：當人類價值觀衝突時的家庭機器人評估

摘要

雖然家用機器人常以任務完成度來評估，但日常居家環境中存在價值衝突的情況，在這些情境中，機器人應選擇優先考量其他價值（如人類自主性、效率或社會適切性）的行動，而非僅以任務成功為目標。然而，目前尚無用於評估機器人在此類情境中價值偏好的基準。我們提出RobotValues，這是一個在10,000個價值衝突情境中評估家用機器人規劃能力的基準。每個實例包含一張逼真的居家影像，以及多個分別優先考量不同人類價值且可行的機器人行動。我們透過LLM輔助情境生成、利害關係人基礎的價值提取、影像生成及自動品質控管來建構RobotValues。利用RobotValues，我們評估了機器人領域使用的視覺語言模型，結果發現模型展現出預設的價值偏好，包括安全與順應性，但卻忽略了優先考量隱私的行動。當模型被指示優先考量與其自身偏好衝突的特定價值時，往往無法覆蓋其預設行動，在80%的情況下選擇錯誤的行動。這些研究結果顯示，家用機器人的評估不僅應衡量任務完成度或安全合規性，還應評估機器人能否在人類價值發生衝突時，於可行的行動之間做出選擇。

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.