Nested Training for Mutual Adaptation in Human-AI Teaming

TL;DR

This paper introduces a nested training approach for human-AI teaming that models human adaptation explicitly, leading to more adaptable and effective robots in cooperative tasks.

Contribution

The paper proposes a nested training regime based on I-POMDPs to train agents that better adapt to human behavior without developing opaque implicit coordination.

Findings

Our method outperforms baselines in task success with unseen adaptive partners.

Agents trained with nested training show greater adaptability in team interactions.

The approach effectively captures human-like adaptive behaviors in robots.

Abstract

Mutual adaptation is a central challenge in human--AI teaming, as humans naturally adjust their strategies in response to a robot's policy. Existing approaches aim to improve diversity in training partners to approximate human behavior, but these partners are static and fail to capture adaptive behavior of humans. Exposing robots to adaptive behaviors is critical, yet when both agents learn simultaneously in a multi-agent setting, they often converge to opaque implicit coordination strategies that only work with the agents they were co-trained with. Such agents fail to generalize when paired with new partners. In order to capture the adaptive behavior of humans, we model the human-robot teaming scenario as an Interactive Partially Observable Markov Decision Process (I-POMDP), explicitly modeling human adaptation as part of the state. We propose a nested training regime to approximately learn the solution to a finite-level I-POMDP. In this framework, agents at each level are trained against adaptive agents from the level below. This ensures that the ego agent is exposed to adaptive behavior during training while avoiding the emergence of implicit coordination strategies, since the training partners are not themselves learning. We train our method in a multi-episode, required cooperation setup in the Overcooked domain, comparing it against several baseline agents designed for human-robot teaming. We evaluate the performance of our agent when paired with adaptive partners that were not seen during training. Our results demonstrate that our agent not only achieves higher task performance with these adaptive partners but also exhibits significantly greater adaptability during team interactions.