Mathematical Models of Adaptation in Human-Robot Collaboration

TL;DR

This paper reviews probabilistic planning and game-theoretic methods for enabling robots to adapt and reason under uncertainty during human-robot collaboration across various real-world settings.

Contribution

It provides a comprehensive overview of models and algorithms that incorporate uncertainty and human adaptation in collaborative robotic systems.

Findings

Probabilistic planning enables robots to handle uncertainty in human-robot interactions.

Game-theoretic algorithms facilitate reasoning about human internal states and adaptation.

Diverse robot behaviors emerge in real-time interactions across multiple application domains.

Abstract

A robot operating in isolation needs to reason over the uncertainty in its model of the world and adapt its own actions to account for this uncertainty. Similarly, a robot interacting with people needs to reason over its uncertainty over the human internal state, as well as over how this state may change, as humans adapt to the robot. This paper summarizes our own work in this area, which depicts the different ways that probabilistic planning and game-theoretic algorithms can enable such reasoning in robotic systems that collaborate with people. We start with a general formulation of the problem as a two-player game with incomplete information. We then articulate the different assumptions within this general formulation, and we explain how these lead to exciting and diverse robot behaviors in real-time interactions with actual human subjects, in a variety of manufacturing, personal robotics and assistive care settings.