Human but not robotic gaze facilitates action prediction

The Bodily Appearance of a Virtual Partner Affects the Activity of the Action Observation and Action Monitoring Systems in a Minimally Interactive Task

One pending question in social neuroscience is whether interpersonal interactions are processed differently by the brain depending on the bodily characteristics of the interactor, i.e., their physical appearance. To address this issue, we engaged participants in a minimally interactive task with an avatar either showing bodily features or not while recording their brain activity using electroencephalography (EEG) in order to investigate indices of action observation and action monitoring processing. Multivariate results showed that bodily compared with nonbodily appearance modulated parieto-occipital neural patterns throughout the entire duration of the observed movement and that, importantly, such patterns differ from the ones related to initial shape processing. Furthermore, among the electrocortical indices of action monitoring, only the early observational positivity (oPe) was responsive to the bodily appearance of the observed agent under the specific task requirement to predict the partner movement. Taken together, these findings broaden the understanding of how bodily appearance shapes the spatiotemporal processing of an interactor's movements. This holds particular relevance in our modern society, where human-artificial (virtual or robotic) agent interactions are rapidly becoming ubiquitous.

Body Form Modulates the Prediction of Human and Artificial Behaviour from Gaze Observation

The future of human–robot collaboration relies on people’s ability to understand and predict robots' actions. The machine-like appearance of robots, as well as contextual information, may influence people’s ability to anticipate the behaviour of robots. We conducted six separate experiments to investigate how spatial cues and task instructions modulate people’s ability to understand what a robot is doing. Participants observed goal-directed and non-goal directed gaze shifts made by human and robot agents, as well as directional cues displayed by a triangle. We report that biasing an observer's attention, by showing just one object an agent can interact with, can improve people’s ability to understand what humanoid robots will do. Crucially, this cue had no impact on people’s ability to predict the upcoming behaviour of the triangle. Moreover, task instructions that focus on the visual and motor consequences of the observed gaze were found to influence mentalising abilities. We suggest that the human-like shape of an agent and its physical capabilities facilitate the prediction of an upcoming action. The reported findings expand current models of gaze perception and may have important implications for human–human and human–robot collaboration.

A Critical Analysis of Industrial Human-Robot Communication and Its Quest for Naturalness Through the Lens of Complexity Theory

Human-robot communication is one of the actively researched fields to enable efficient and seamless collaboration between a human and an intelligent industrial robotic system. The field finds its roots in human communication with the aim to achieve the “naturalness” inherent in the latter. Industrial human-robot communication pursues communication with simplistic commands and gestures, which is not representative of an uncontrolled real-world industrial environment. In addition, naturalness in communication is a consequence of its dynamism, typically ignored as a design criterion in industrial human-robot communication. Complexity Theory-based natural communication models allow for a more accurate representation of human communication which, when adapted, could also benefit the field of human-robot communication. This paper presents a perspective by reviewing the state of human-robot communication in industrial settings and then presents a critical analysis of the same through the lens of Complexity Theory. Furthermore, the work identifies research gaps in the aforementioned field, fulfilling which, would propel the field towards a truly natural form of communication. Finally, the work briefly discusses a general framework that leverages the experiential learning of data-based techniques and naturalness of human knowledge.