Preference Elicitation and Incorporation for Human-Robot Task Scheduling

In this work, we address the challenge of incorporating human preferences into the task-scheduling process for human-robot teams. Humans have various individual preferences that can be influenced by context and situational information. Incorporating these preferences can lead to improved team performance. Our main contribution is a framework that helps elicit and incorporate preferences during task scheduling. We achieve this by proposing 1) a constraint programming method to generate a range of plans, 2) an intelligent approach for selecting and presenting task schedules based on task features, and 3) a preference incorporation method that uses large language models to convert preferences into soft constraints. Our results demonstrate that we can efficiently generate diverse plans for preference elicitation and incorporate them into the task-scheduling process. We evaluate our framework using an assembly-inspired case study and show how it can effectively incorporate complex and realistic preferences. Our implementation can be found at github.com/RROS-Lab/Human-Robot-Preference-Planning.

2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)