32,385 research outputs found
Human-Robot Dichotomy
This paper belongs to the area of roboethics and responsible robotics. It discusses the conceptual and practical separation of humans and robots in designing and implementing robots into real-world environments. We argue here that humans are often seen as a component that is only optional in design thinking, and in some cases even an obstacle to the successful robot performance. Such an approach may vary from viewing humans as a factor that does not belong to the robotics domain, through attempts to ‘adjust’ humans to robot requirements, to the overall replacement of humans with robots. Such separation or exclusion of humans poses serious ethical challenges, including the very exclusion of ethics from our thinking about robots
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
Sympathy Begins with a Smile, Intelligence Begins with a Word: Use of Multimodal Features in Spoken Human-Robot Interaction
Recognition of social signals, from human facial expressions or prosody of
speech, is a popular research topic in human-robot interaction studies. There
is also a long line of research in the spoken dialogue community that
investigates user satisfaction in relation to dialogue characteristics.
However, very little research relates a combination of multimodal social
signals and language features detected during spoken face-to-face human-robot
interaction to the resulting user perception of a robot. In this paper we show
how different emotional facial expressions of human users, in combination with
prosodic characteristics of human speech and features of human-robot dialogue,
correlate with users' impressions of the robot after a conversation. We find
that happiness in the user's recognised facial expression strongly correlates
with likeability of a robot, while dialogue-related features (such as number of
human turns or number of sentences per robot utterance) correlate with
perceiving a robot as intelligent. In addition, we show that facial expression,
emotional features, and prosody are better predictors of human ratings related
to perceived robot likeability and anthropomorphism, while linguistic and
non-linguistic features more often predict perceived robot intelligence and
interpretability. As such, these characteristics may in future be used as an
online reward signal for in-situ Reinforcement Learning based adaptive
human-robot dialogue systems.Comment: Robo-NLP workshop at ACL 2017. 9 pages, 5 figures, 6 table
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