Robot Tasks with Fuzzy Time Requirements from Natural Language Instructions

TL;DR

This paper introduces fuzzy skills for robots that interpret natural language instructions with vague time requirements, enabling flexible scheduling based on user satisfaction modeled by satisfaction functions.

Contribution

It proposes a novel approach using satisfaction functions to handle fuzzy time constraints in robot instructions, supported by a user study to determine optimal function shapes.

Findings

Trapezoidal functions best approximate user satisfaction.

Users are more lenient with future execution times.

Fuzzy skills enable flexible and user-aligned robot scheduling.

Abstract

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.