Humans have the fascinating capacity of processing nonverbal 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 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 analyze the importance of different nonverbal cues for action perception. As our second contribution, we used 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 (nonverbal) signals exchanged by humans during interaction, and how to incorporate such a mechanism in robotic systems with the twin goal of being able to “read” human action intentionsand acting in a way that is legible by humans.

When humans interact with each other, eye gaze movements have to support motor control as well as communication. On the one hand, we need to fixate the task goal to retrieve visual information required for safe and precise action-execution. On the other hand, gaze movements fulfil the purpose of communication, both for reading the intention of our interaction partners, as well as to signal our action intentions to others. We study this Gaze Dialogue between two participants working on a collaborative task involving two types of actions: 1) individual action and 2) action-in-interaction . We recorded the eye-gaze data of both participants during the interaction sessions in order to build a computational model, the Gaze Dialogue , encoding the interplay of the eye movements during the dyadic interaction. The model also captures the correlation between the different gaze fixation points and the nature of the action. This knowledge is used to infer the type of action performed by an individual. We validated the model against the recorded eye-gaze behavior of one subject, taking the eye-gaze behavior of the other subject as the input. Finally, we used the model to design a humanoid robot controller that provides interpersonal gaze coordination in human–robot interaction scenarios. During the interaction, the robot is able to: 1) adequately infer the human action from gaze cues; 2) adjust its gaze fixation according to the human eye-gaze behavior; and 3) signal nonverbal cues that correlate with the robot’s own action intentions.

Observing how humans interact with each other and how they manipulate objects, offer insight on interaction mechanisms and how these are influenced by the physical properties of the used objects. These insights can support a more informed design of robot controllers meant to manipulate objects in collaboration with humans. In this work we study human-to-human handovers of cups filled with various amount of liquid and textures, and investigate to which extent the manipulation strategy depends on: (i) the individual preference, (ii) whether the cup is filled with water or not, and (iii) the cup physical properties. An analysis of the human giver’s hand acceleration, velocity, and position during the handover of different cups under two liquid level conditions, allows to distinguish between careful and not-careful (normal) manipulation. We quantify to which extent the liquid level inside the cups influences the carefulness level of human manipulation. Lastly, our study reveals that the cups’ physical properties, such as fragility, breakability, and deformability, play a role in shaping the carefulness of the manipulation. We apply these findings to human-robot scenarios by developing a robot controller capable of detecting, in real-time, if the human is being more careful than normal, and adapting the robot’s approach of interaction accordingly. Additionally, we show that the detection of a careful manipulation, depending on the experimental context, may provide the robot with information concerning the human partner’s intention or need for (manipulation) assistance.

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.