# Game-Theoretic Modeling of Human Adaptation in Human-Robot Collaboration

## TL;DR

This paper introduces a game-theoretic model for human adaptation in human-robot teams, enabling robots to optimize actions that improve team performance by accounting for human learning and expectations.

## Contribution

It develops a novel game-theoretic framework modeling partial human adaptation and provides efficient algorithms for robot decision-making in collaborative settings.

## Key findings

- The model improves team performance in experiments.
- Optimal policies can be computed efficiently.
- Human-robot collaboration is enhanced with the proposed approach.

## Abstract

In human-robot teams, humans often start with an inaccurate model of the robot capabilities. As they interact with the robot, they infer the robot's capabilities and partially adapt to the robot, i.e., they might change their actions based on the observed outcomes and the robot's actions, without replicating the robot's policy. We present a game-theoretic model of human partial adaptation to the robot, where the human responds to the robot's actions by maximizing a reward function that changes stochastically over time, capturing the evolution of their expectations of the robot's capabilities. The robot can then use this model to decide optimally between taking actions that reveal its capabilities to the human and taking the best action given the information that the human currently has. We prove that under certain observability assumptions, the optimal policy can be computed efficiently. We demonstrate through a human subject experiment that the proposed model significantly improves human-robot team performance, compared to policies that assume complete adaptation of the human to the robot.