发表机构
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen; Shenzhen Research Institute of Big Data (SRIBD)（香港中文大学（深圳）理工学院; 深圳大数据研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究推出真实机器人基准One Word, Different Action，评估具身系统的决策不变性与敏感性，发现现代模型在多任务约束整合为机器人动作时存在明显退化，核心挑战是组合多任务需求为正确决策。
AI 中文摘要
自然语言指令的改变可直接改变机器人的行为。一个可靠的具身系统应在任务不变时保持其动作，在任务本身改变时正确更新动作。我们推出One Word, Different Action，这是一个基于物理决策状态与可执行动作的真实机器人基准，使用任务保留型与任务改变型指令对，共同评估决策不变性与决策敏感性，还会在多约束推理和真实RGB grounding下进行进一步评估。实验表明，现代模型在单约束指令改变上接近饱和，但当必须将多个任务约束整合为一个可执行决策时，部分模型会明显退化。这些结果表明，更突出的剩余挑战不再是识别孤立的指令改变，而是可靠地将多个任务需求组合为正确的机器人动作决策。
英文摘要
Changes in natural-language instructions can directly alter the behavior ultimately executed by a robot, but such changes do not always imply that the task itself has changed. We propose One Word, Different Action, a language-conditioned executable decision benchmark based on real-robot physical decision anchors. It evaluates robot behavioral responses under task-preserving and task-changing conditions and examines joint reasoning over multiple task constraints. The benchmark uses decision invariance and decision sensitivity to measure action stability when task semantics are preserved and correct decision updates when task requirements change. Matched atomic constituents directly relate correct handling of individual constraints to the corresponding joint executable decision. Experiments show that atomic-task correctness does not sufficiently represent compositional reasoning ability: even when every constituent constraint is handled correctly, a model may still produce an erroneous final action decision on the corresponding compositional task. These results indicate that the key challenge in language-conditioned robot reasoning lies not only in understanding individual task requirements, but also in integrating multiple correctly handled constraints into consistent executable behavior.
