Self-Supervised Prediction of the Intention to Interact with a Service Robot

TL;DR

This paper presents a self-supervised learning approach for service robots to predict human interaction intentions before they occur, enabling more proactive and smoother interactions in real-world scenarios.

Contribution

It introduces a self-supervised classification method using pose and motion features to predict user interaction intent with high accuracy before contact.

Findings

Achieves AUROC > 0.9 in predicting interaction intent more than 3 seconds in advance.

Validates the approach in three diverse real-world scenarios with natural human behavior.

Demonstrates effectiveness without external supervision in challenging environments.

Abstract

A service robot can provide a smoother interaction experience if it has the ability to proactively detect whether a nearby user intends to interact, in order to adapt its behavior e.g. by explicitly showing that it is available to provide a service. In this work, we propose a learning-based approach to predict the probability that a human user will interact with a robot before the interaction actually begins; the approach is self-supervised because after each encounter with a human, the robot can automatically label it depending on whether it resulted in an interaction or not. We explore different classification approaches, using different sets of features considering the pose and the motion of the user. We validate and deploy the approach in three scenarios. The first collects $3442$ natural sequences (both interacting and non-interacting) representing employees in an office break area: a real-world, challenging setting, where we consider a coffee machine in place of a service robot. The other two scenarios represent researchers interacting with service robots ($200$ and $72$ sequences, respectively). Results show that, even in challenging real-world settings, our approach can learn without external supervision, and can achieve accurate classification (i.e. AUROC greater than $0.9$) of the user's intention to interact with an advance of more than $3$s before the interaction actually occurs.