1,641 research outputs found
Early Turn-taking Prediction with Spiking Neural Networks for Human Robot Collaboration
Turn-taking is essential to the structure of human teamwork. Humans are
typically aware of team members' intention to keep or relinquish their turn
before a turn switch, where the responsibility of working on a shared task is
shifted. Future co-robots are also expected to provide such competence. To that
end, this paper proposes the Cognitive Turn-taking Model (CTTM), which
leverages cognitive models (i.e., Spiking Neural Network) to achieve early
turn-taking prediction. The CTTM framework can process multimodal human
communication cues (both implicit and explicit) and predict human turn-taking
intentions in an early stage. The proposed framework is tested on a simulated
surgical procedure, where a robotic scrub nurse predicts the surgeon's
turn-taking intention. It was found that the proposed CTTM framework
outperforms the state-of-the-art turn-taking prediction algorithms by a large
margin. It also outperforms humans when presented with partial observations of
communication cues (i.e., less than 40% of full actions). This early prediction
capability enables robots to initiate turn-taking actions at an early stage,
which facilitates collaboration and increases overall efficiency.Comment: Submitted to IEEE International Conference on Robotics and Automation
(ICRA) 201
Spotting Agreement and Disagreement: A Survey of Nonverbal Audiovisual Cues and Tools
While detecting and interpreting temporal patterns of nonâverbal behavioral cues in a given context is a natural and often unconscious process for humans, it remains a rather difficult task for computer systems. Nevertheless, it is an important one to achieve if the goal is to realise a naturalistic communication between humans and machines. Machines that are able to sense social attitudes like agreement and disagreement and respond to them in a meaningful way are likely to be welcomed by users due to the more natural, efficient and humanâcentered interaction they are bound to experience. This paper surveys the nonverbal cues that could be present during agreement and disagreement behavioural displays and lists a number of tools that could be useful in detecting them, as well as a few publicly available databases that could be used to train these tools for analysis of spontaneous, audiovisual instances of agreement and disagreement
First impressions: A survey on vision-based apparent personality trait analysis
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft
A Review of Verbal and Non-Verbal Human-Robot Interactive Communication
In this paper, an overview of human-robot interactive communication is
presented, covering verbal as well as non-verbal aspects of human-robot
interaction. Following a historical introduction, and motivation towards fluid
human-robot communication, ten desiderata are proposed, which provide an
organizational axis both of recent as well as of future research on human-robot
communication. Then, the ten desiderata are examined in detail, culminating to
a unifying discussion, and a forward-looking conclusion
Requirements for Robotic Interpretation of Social Signals âin the Wildâ: Insights from Diagnostic Criteria of Autism Spectrum Disorder
The last few decades have seen widespread advances in technological means to characterise
observable aspects of human behaviour such as gaze or posture. Among others, these developments
have also led to significant advances in social robotics. At the same time, however, social robots
are still largely evaluated in idealised or laboratory conditions, and it remains unclear whether
the technological progress is sufficient to let such robots move âinto the wildâ. In this paper, we
characterise the problems that a social robot in the real world may face, and review the technological
state of the art in terms of addressing these. We do this by considering what it would entail
to automate the diagnosis of Autism Spectrum Disorder (ASD). Just as for social robotics, ASD
diagnosis fundamentally requires the ability to characterise human behaviour from observable
aspects. However, therapists provide clear criteria regarding what to look for. As such, ASD diagnosis
is a situation that is both relevant to real-world social robotics and comes with clear metrics. Overall,
we demonstrate that even with relatively clear therapist-provided criteria and current technological
progress, the need to interpret covert behaviour cannot yet be fully addressed. Our discussions have
clear implications for ASD diagnosis, but also for social robotics more generally. For ASD diagnosis,
we provide a classification of criteria based on whether or not they depend on covert information
and highlight present-day possibilities for supporting therapists in diagnosis through technological
means. For social robotics, we highlight the fundamental role of covert behaviour, show that the
current state-of-the-art is unable to characterise this, and emphasise that future research should tackle
this explicitly in realistic settings
The effect of conversational agent skill on user behavior during deception
Conversational agents (CAs) are an integral component of many personal and business interactions. Many recent advancements in CA technology have attempted to make these interactions more natural and human-like. However, it is currently unclear how human-like traits in a CA impact the way users respond to questions from the CA. In some applications where CAs may be used, detecting deception is important. Design elements that make CA interactions more human-like may induce undesired strategic behaviors from human deceivers to mask their deception. To better understand this interaction, this research investigates the effect of conversational skillâthat is, the ability of the CA to mimic human conversationâfrom CAs on behavioral indicators of deception. Our results show that cues of deception vary depending on CA conversational skill, and that increased conversational skill leads to users engaging in strategic behaviors that are detrimental to deception detection. This finding suggests that for applications in which it is desirable to detect when individuals are lying, the pursuit of more human-like interactions may be counter-productive
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