2,088 research outputs found
An Intervening Ethical Governor for a Robot Mediator in Patient-Caregiver Relationships
© Springer International Publishing AG 2015DOI: 10.1007/978-3-319-46667-5_6Patients with Parkinsonâs disease (PD) experience challenges when interacting with
caregivers due to their declining control over their musculature. To remedy those challenges, a
robot mediator can be used to assist in the relationship between PD patients and their caregivers.
In this context, a variety of ethical issues can arise. To overcome one issue in particular,
providing therapeutic robots with a robot architecture that can ensure patientsâ and caregiversâ
dignity is of potential value. In this paper, we describe an intervening ethical governor for a
robot that enables it to ethically intervene, both to maintain effective patientâcaregiver
relationships and prevent the loss of dignity
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
Integrating verbal and nonverbal communication in a dynamic neural field architecture for humanârobot interaction
How do humans coordinate their intentions, goals and motor behaviors when performing joint action tasks? Recent experimental evidence suggests that resonance processes in the observerâs motor system are crucially involved in our ability to understand actions of othersâ, to infer their goals and even to comprehend their action-related language. In this paper, we present a control architecture for humanârobot collaboration that exploits this close perception-action linkage as a means to achieve more natural and efficient communication grounded in sensorimotor experiences. The architecture is formalized by a coupled system of dynamic neural fields representing a distributed network of neural populations that encode in their activation patterns goals, actions and shared task knowledge. We validate the verbal and nonverbal communication skills of the robot in a joint assembly task in which the humanârobot team has to construct toy objects from their components. The experiments focus on the robotâs capacity to anticipate the userâs needs and to detect and communicate unexpected events that may occur during joint task execution.Fundação para a CiĂȘncia e a Tecnologia (FCT) - Bolsa POCI/V.5/A0119/2005 and CONC-REEQ/17/2001European Commission through the project JAST (IP-003747
Motion for cooperation and vitality in Human-robot interaction
In social interactions, human movement is a rich source of information for all those who
take part in the collaboration. In fact, a variety of intuitive messages are communicated
through motion and continuously inform the partners about the future unfolding of the
actions. A similar exchange of implicit information could support movement coordination
in the context of Human-Robot Interaction. Also the style of an action, i.e. the way it is
performed, has a strong influence on interaction between humans. The same gesture has
different consequences when it is performed aggressively or kindly, and humans are very
sensitive to these subtle differences in others\u2019 behaviors. During the three years of my
PhD, I focused on these two aspects of human motion. In a firs study, we investigated how
implicit signaling in an interaction with a humanoid robot can lead to emergent coordination
in the form of automatic speed adaptation. In particular, we assessed whether different
cultures \u2013 specifically Japanese and Italian \u2013 have a different impact on motor resonance and
synchronization in HRI. Japanese people show a higher general acceptance toward robots
when compared with Western cultures. Since acceptance, or better affiliation, is tightly
connected to imitation and mimicry, we hypothesized a higher degree of speed imitation for
Japanese participants when compared to Italians. In the experimental studies undertaken
both in Japan and Italy,we observed that cultural differences do not impact on the natural
predisposition of subjects to adapt to the robot. In a second study, we investigated how to
endow a humanoid robot with behaviors expressing different vitality forms, by modulating
robot action kinematics and voice. Drawing inspiration from humans, we modified actions
and voice commands performed by the robot to convey an aggressive or kind attitude. In
a series of experiments we demonstrated that the humanoid was consistently perceived as
aggressive or kind. Human behavior changed in response to the different robot attitudes and
matched the behavior of iCub, in fact participants were faster when the robot was aggressive
and slower when the robot was gentle. The opportunity of humanoid behavior to express
vitality enriches the array of nonverbal communication that can be exploited by robots to
foster seamless interaction. Such behavior might be crucial in emergency and in authoritative
situations in which the robot should instinctively be perceived as assertive and in charge, as
in case of police robots or teachers
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