Coordination with Humans via Strategy Matching

TL;DR

This paper introduces a computational framework enabling robots to recognize human task strategies and adapt their behavior accordingly, improving collaboration efficiency in team tasks.

Contribution

It presents a novel strategy recognition algorithm and a Mixture-of-Experts model for robot adaptation in human-robot collaboration.

Findings

Improved task performance in collaborative cooking tasks

Enhanced collaborative fluency with human partners

Outperforms existing reinforcement learning methods

Abstract

Human and robot partners increasingly need to work together to perform tasks as a team. Robots designed for such collaboration must reason about how their task-completion strategies interplay with the behavior and skills of their human team members as they coordinate on achieving joint goals. Our goal in this work is to develop a computational framework for robot adaptation to human partners in human-robot team collaborations. We first present an algorithm for autonomously recognizing available task-completion strategies by observing human-human teams performing a collaborative task. By transforming team actions into low dimensional representations using hidden Markov models, we can identify strategies without prior knowledge. Robot policies are learned on each of the identified strategies to construct a Mixture-of-Experts model that adapts to the task strategies of unseen human partners. We evaluate our model on a collaborative cooking task using an Overcooked simulator. Results of an online user study with 125 participants demonstrate that our framework improves the task performance and collaborative fluency of human-agent teams, as compared to state of the art reinforcement learning methods.