1,036 research outputs found
NICOL: A Neuro-inspired Collaborative Semi-humanoid Robot that Bridges Social Interaction and Reliable Manipulation
Robotic platforms that can efficiently collaborate with humans in physical
tasks constitute a major goal in robotics. However, many existing robotic
platforms are either designed for social interaction or industrial object
manipulation tasks. The design of collaborative robots seldom emphasizes both
their social interaction and physical collaboration abilities. To bridge this
gap, we present the novel semi-humanoid NICOL, the Neuro-Inspired COLlaborator.
NICOL is a large, newly designed, scaled-up version of its well-evaluated
predecessor, the Neuro-Inspired COmpanion (NICO). NICOL adopts NICO's head and
facial expression display and extends its manipulation abilities in terms of
precision, object size, and workspace size. Our contribution in this paper is
twofold -- firstly, we introduce the design concept for NICOL, and secondly, we
provide an evaluation of NICOL's manipulation abilities by presenting a novel
extension for an end-to-end hybrid neuro-genetic visuomotor learning approach
adapted to NICOL's more complex kinematics. We show that the approach
outperforms the state-of-the-art Inverse Kinematics (IK) solvers KDL, TRACK-IK
and BIO-IK. Overall, this article presents for the first time the humanoid
robot NICOL, and contributes to the integration of social robotics and neural
visuomotor learning for humanoid robots
Affective Communication for Socially Assistive Robots (SARs) for Children with Autism Spectrum Disorder: A Systematic Review
Research on affective communication for socially assistive robots has been conducted to
enable physical robots to perceive, express, and respond emotionally. However, the use of affective
computing in social robots has been limited, especially when social robots are designed for children,
and especially those with autism spectrum disorder (ASD). Social robots are based on cognitiveaffective models, which allow them to communicate with people following social behaviors and
rules. However, interactions between a child and a robot may change or be different compared to
those with an adult or when the child has an emotional deficit. In this study, we systematically
reviewed studies related to computational models of emotions for children with ASD. We used the
Scopus, WoS, Springer, and IEEE-Xplore databases to answer different research questions related to
the definition, interaction, and design of computational models supported by theoretical psychology
approaches from 1997 to 2021. Our review found 46 articles; not all the studies considered children
or those with ASD.This research was funded by VRIEA-PUCV, grant number 039.358/202
Socially Assistive Robots for Older Adults and People with Autism: An Overview
Over one billion people in the world suffer from some form of disability. Nevertheless, according to the World Health Organization, people with disabilities are particularly vulnerable to deficiencies in services, such as health care, rehabilitation, support, and assistance. In this sense, recent technological developments can mitigate these deficiencies, offering less-expensive assistive systems to meet users’ needs. This paper reviews and summarizes the research efforts toward the development of these kinds of systems, focusing on two social groups: older adults and children with autism.This research was funded by the Spanish Government TIN2016-76515-R grant for the COMBAHO project, supported with Feder funds. It has also been supported by Spanish grants for PhD studies ACIF/2017/243 and FPU16/00887
Affective Computing for Human-Robot Interaction Research: Four Critical Lessons for the Hitchhiker
Social Robotics and Human-Robot Interaction (HRI) research relies on
different Affective Computing (AC) solutions for sensing, perceiving and
understanding human affective behaviour during interactions. This may include
utilising off-the-shelf affect perception models that are pre-trained on
popular affect recognition benchmarks and directly applied to situated
interactions. However, the conditions in situated human-robot interactions
differ significantly from the training data and settings of these models. Thus,
there is a need to deepen our understanding of how AC solutions can be best
leveraged, customised and applied for situated HRI. This paper, while
critiquing the existing practices, presents four critical lessons to be noted
by the hitchhiker when applying AC for HRI research. These lessons conclude
that: (i) The six basic emotions categories are irrelevant in situated
interactions, (ii) Affect recognition accuracy (%) improvements are
unimportant, (iii) Affect recognition does not generalise across contexts, and
(iv) Affect recognition alone is insufficient for adaptation and
personalisation. By describing the background and the context for each lesson,
and demonstrating how these lessons have been learnt, this paper aims to enable
the hitchhiker to successfully and insightfully leverage AC solutions for
advancing HRI research.Comment: 11 pages, 3 figures, 1 tabl
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