Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration

TL;DR

This paper demonstrates that integrating goal inference via Bayesian methods with real-time task re-planning enhances human-robot collaboration, leading to better objective performance and higher user satisfaction.

Contribution

It introduces a novel approach combining goal inference and dynamic re-planning in robots, improving collaboration effectiveness without explicit communication.

Findings

Participants preferred robots that adapted to their actions.

Goal inference significantly improved task success rates.

Perceived team performance increased with adaptive robot behavior.

Abstract

The study of human-robot interaction is fundamental to the design and use of robotics in real-world applications. Robots will need to predict and adapt to the actions of human collaborators in order to achieve good performance and improve safety and end-user adoption. This paper evaluates a human-robot collaboration scheme that combines the task allocation and motion levels of reasoning: the robotic agent uses Bayesian inference to predict the next goal of its human partner from his or her ongoing motion, and re-plans its own actions in real time. This anticipative adaptation is desirable in many practical scenarios, where humans are unable or unwilling to take on the cognitive overhead required to explicitly communicate their intent to the robot. A behavioral experiment indicates that the combination of goal inference and dynamic task planning significantly improves both objective and perceived performance of the human-robot team. Participants were highly sensitive to the differences between robot behaviors, preferring to work with a robot that adapted to their actions over one that did not.