Learning Human Contribution Preferences in Collaborative Human-Robot Tasks

Michelle D Zhao, Reid Simmons, Henny Admoni

Keywords: human-robot collaboration, reward learning, human-robot interaction

TL;DR: We propose an interaction paradigm and approach for learning human contribution constraints in a collaborative human-robot task, where successful teams maximize task objectives while adhering to human and robot constraints.

Abstract: In human-robot collaboration, both human and robotic agents must work together to achieve a set of shared objectives. However, each team member may have individual preferences, or constraints, for how they would like to contribute to the task. Effective teams align their actions to optimize task performance while satisfying each team member's constraints to the greatest extent possible. We propose a framework for representing human and robot contribution constraints in collaborative human-robot tasks. Additionally, we present an approach for learning a human partner's contribution constraint online during a collaborative interaction. We evaluate our approach using a variety of simulated human partners in a collaborative decluttering task. Our results demonstrate that our method improves team performance over baselines with some, but not all, simulated human partners. Furthermore, we conducted a pilot user study to gather preliminary insights into the effectiveness of our approach on task performance and collaborative fluency. Preliminary results suggest that pilot users performed fluently with our method, motivating further investigation into considering preferences that emerge from collaborative interactions.

Student First Author: yes

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Video: https://youtu.be/JM4cMHZSu20