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.Comment: 2022 IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS 2022