Negotiation of assignation plans in human-robot team task scheduling

Abstract

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In recent years, considerable attention has been given to improving human-robot collaboration. Despite advances in robotic capabilities and interaction techniques, achieving a fair distribution of tasks remains challenging due to the dynamic nature of human preferences and situational constraints. This paper presents a novel negotiation framework that enables robots to effectively communicate with humans to facilitate fair and adaptive task allocation. Our approach leverages automated planning techniques with the Planning Domain Definition Language (PDDL), explicitly encoding tasks, constraints, and preferences from both human and robotic perspectives. Task allocation is optimized based on three key criteria: the robot’s effort, the human’s effort, and overall task success. Additionally, we integrate a Natural Language Processing (NLP) model that interprets human preferences and informs the negotiation process, ensuring that the robot generates task proposals aligned with human input. The negotiation follows an alternating-offer protocol, with the robot employing a sigmoid conceder strategy to iteratively refine task allocation, leading to balanced and mutually acceptable plans. To evaluate our approach, we conduct a comprehensive user study with non-trained volunteers interacting with the robot, assessing the effectiveness, fairness, and adaptability of the proposed system in real-world scenarios.

This work was partially supported by JST Moonshot R, D grant number JPMJMS2011 and LENA project (PID2022-142039NA-I00), funded by MCIN/AEI/10.13039/501100011033.

Peer Reviewed

Postprint (author's final draft)