Action Anticipation: Reading the Intentions of Humans and Robots

TL;DR

This paper presents a dataset, a computational model, and a robotic implementation to understand and predict human and robot action intentions through non-verbal cues like body motion and gaze.

Contribution

It introduces a new dataset, a model of human interaction cues, and validates intention reading in robots during interpersonal interactions.

Findings

Model can interpret non-verbal signals exchanged during interaction

Robot can predict human intentions using the model

Model enhances robot's action legibility to humans

Abstract

Humans have the fascinating capacity of processing non-verbal visual cues to understand and anticipate the actions of other humans. This "intention reading" ability is underpinned by shared motor-repertoires and action-models, which we use to interpret the intentions of others as if they were our own. We investigate how the different cues contribute to the legibility of human actions during interpersonal interactions. Our first contribution is a publicly available dataset with recordings of human body-motion and eye-gaze, acquired in an experimental scenario with an actor interacting with three subjects. From these data, we conducted a human study to analyse the importance of the different non-verbal cues for action perception. As our second contribution, we used the motion/gaze recordings to build a computational model describing the interaction between two persons. As a third contribution, we embedded this model in the controller of an iCub humanoid robot and conducted a second human study, in the same scenario with the robot as an actor, to validate the model's "intention reading" capability. Our results show that it is possible to model (non-verbal) signals exchanged by humans during interaction, and how to incorporate such a mechanism in robotic systems with the twin goal of : (i) being able to "read" human action intentions, and (ii) acting in a way that is legible by humans.