I Reach Faster When I See You Look: Gaze Effects in Human–Human and Human–Robot Face-to-Face Cooperation

Abstract

Human–human interaction in natural environments relies on a variety of perceptual cues. Humanoid robots are becoming increasingly refined in their sensorimotor capabilities, and thus should now be able to manipulate and exploit these social cues in cooperation with their human partners. Previous studies have demonstrated that people follow human and robot gaze, and that it can help them to cope with spatially ambiguous language. Our goal is to extend these findings into the domain of action, to determine how human and robot gaze can influence the speed and accuracy of human action. We report on results from a human–human cooperation experiment demonstrating that an agent’s vision of her/his partner’s gaze can significantly improve that agent’s performance in a cooperative task. We then implement a heuristic capability to generate such gaze cues by a humanoid robot that engages in the same cooperative interaction. The subsequent human–robot experiments demonstrate that a human agent can indeed exploit the predictive gaze of their robot partner in a cooperative task. This allows us to render the humanoid robot more human-like in its ability to communicate with humans. The long term objectives of the work are thus to identify social cooperation cues, and to validate their pertinence through implementation in a cooperative robot. The current research provides the robot with the capability to produce appropriate speech and gaze cues in the context of human–robot cooperation tasks. Gaze is manipulated in three conditions: Full gaze (coordinated eye and head), eyes hidden with sunglasses, and head fixed. We demonstrate the pertinence of these cues in terms of statistical measures of action times for humans in the context of a cooperative task, as gaze significantly facilitates cooperation as measured by human response times.

Looking for Conflict: Gaze Dynamics in a Dyadic Mixed-Motive Game

“…6,7,8,9,10,11,12), it appears unlikely that the gaze patterns only served the function of speech synchronisation. This suggests that the differences in gaze patterns are attributable to the differences in the players' motivations and types of action they choose to employ.…”

“…The former was applied mainly to ensure that children on opposite poles (neglected vs. popular) or children who did not like each other were not paired together, given the sensitive nature of this experiment 8 , to avoid undesirable effects on the participants. The paired children were from the same class, hence they knew each other and shared a history.…”

“…Humans' non-verbal behaviours communicate these high-level constructs and allow others to monitor behaviour in real-time for effective cooperation [45]. Gaze, as argued before, is linked to high-level cognitive information and may unveil the course of a person's cognitive activities [8]. As an attempt to disclose the cognitive processes behind the interactions in the "Game of Nines", we look at relevant gaze cues in human-human interactions to disclose the motivations behind certain actions.…”

Detecting Biological Motion for Human–Robot Interaction: A Link between Perception and Action

“…Conversely, the execution of non-biological motion by a humanoid robot has been suggested as a possible cause for the Uncanny Valley effect (Mori, 1970), i.e., to the occurrence of a sense of eeriness and disgust toward the robot, precluding the possibility for a natural interaction (Chaminade and Cheng, 2009). Human-like motion benefits interaction also when it is applied to gaze behavior, for instance facilitating the regulation of conversations (Mutlu et al, 2012), the coordination of shared plans in collaboration (Boucher et al, 2012) and the prediction of robot goals (Rea et al, 2016). Drawing inspiration from these evidence, to maximize the efficacy of the human activity detection module, our proposed architecture leverages the regularities of biological motion also for the preparation and execution of the robot saccadic action.…”

Effective Task Training Strategies for Instructional Robots

“…Robots' success in instruction will depend on their effectiveness first in their use of language, including linguistic and nonverbal cues [2,5,14,22], and second in their presentation of task information, including what information they disclose at a given moment, how they present task information, and how they correct misunderstandings. This paper focuses on the latter problem of effectively presenting task information and explores how robots might adopt the strategies that human instructors use to present task information and what strategies might be most effective.…”