Models of human behavior for human-robot interaction and automated driving: How accurate do the models of human behavior need to be?

TL;DR

This paper discusses how accurate human behavior models need to be for effective human-robot interaction and automated driving, emphasizing goal-oriented modeling and potential risks of overreliance on machine learning.

Contribution

It proposes a framework for focusing on behavior aspects critical to interaction success and highlights the importance of context-specific model accuracy in HRI.

Findings

Models should target behavior aspects most sensitive to interaction success

Identifies challenges in determining which behavior aspects are critical

Warns against overreliance on machine-learned models in HRI

Abstract

There are many examples of cases where access to improved models of human behavior and cognition has allowed creation of robots which can better interact with humans, and not least in road vehicle automation this is a rapidly growing area of research. Human-robot interaction (HRI) therefore provides an important applied setting for human behavior modeling - but given the vast complexity of human behavior, how complete and accurate do these models need to be? Here, we outline some possible ways of thinking about this problem, starting from the suggestion that modelers need to keep the right end goal in sight: A successful human-robot interaction, in terms of safety, performance, and human satisfaction. Efforts toward model completeness and accuracy should be focused on those aspects of human behavior to which interaction success is most sensitive. We emphasise that identifying which those aspects are is a difficult scientific objective in its own right, distinct for each given HRI context. We propose and exemplify an approach to formulating a priori hypotheses on this matter, in cases where robots are to be involved in interactions which currently take place between humans, such as in automated driving. Our perspective also highlights some possible risks of overreliance on machine-learned models of human behavior in HRI, and how to mitigate against those risks.