让机器人理解模糊时间指令，根据用户满意度自动安排任务顺序。
Robot Tasks with Fuzzy Time Requirements from Natural Language Instructions
- 用满意度函数描述用户对任务启动时间的模糊期望
- 实验证明梯形函数最贴近用户真实满意度，且越晚执行越宽容
- 适合需要灵活调度、依赖自然语言指令的智能机器人系统
自然语言使机器人编程对普通人更易用，但其固有的模糊性给传统机器人系统带来挑战。本文聚焦于包含模糊时间要求的指令（如“几分钟后开始”），引入模糊技能概念，通过满意度函数刻画用户对任务启动时间的主观感受。该函数为多任务调度提供时间容忍区间，实现基于满意度的最优安排。通过用户研究，参与者在不同时间点评估任务执行的满意程度，结果表明梯形函数最能拟合实际满意度曲线，且执行时间越靠后，用户容忍度越高。
原文摘要 · Abstract (English)
Natural language allows robot programming to be accessible to everyone. However, the inherent fuzziness in natural language poses challenges for inflexible, traditional robot systems. We focus on instructions with fuzzy time requirements (e.g., "start in a few minutes"). Building on previous robotics research, we introduce fuzzy skills. These define an execution by the robot with so-called satisfaction functions representing vague execution time requirements. Such functions express a user's satisfaction over potential starting times for skill execution. When the robot handles multiple fuzzy skills, the satisfaction function provides a temporal tolerance window for execution, thus, enabling optimal scheduling based on satisfaction. We generalized such functions based on individual user expectations with a user study. The participants rated their satisfaction with an instruction's execution at various times. Our investigations reveal that trapezoidal functions best approximate the users' satisfaction. Additionally, the results suggest that users are more lenient if the execution is specified further into the future.
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