2,799 research outputs found
A Biosymtic (Biosymbiotic Robotic) Approach to Human Development and Evolution. The Echo of the Universe.
In the present work we demonstrate that the current Child-Computer Interaction
paradigm is not potentiating human development to its fullest – it is associated with
several physical and mental health problems and appears not to be maximizing children’s
cognitive performance and cognitive development. In order to potentiate children’s
physical and mental health (including cognitive performance and cognitive development)
we have developed a new approach to human development and evolution.
This approach proposes a particular synergy between the developing human body,
computing machines and natural environments. It emphasizes that children should be
encouraged to interact with challenging physical environments offering multiple possibilities
for sensory stimulation and increasing physical and mental stress to the organism.
We created and tested a new set of computing devices in order to operationalize
our approach – Biosymtic (Biosymbiotic Robotic) devices: “Albert” and “Cratus”. In
two initial studies we were able to observe that the main goal of our approach is being
achieved. We observed that, interaction with the Biosymtic device “Albert”, in a natural
environment, managed to trigger a different neurophysiological response (increases
in sustained attention levels) and tended to optimize episodic memory performance in
children, compared to interaction with a sedentary screen-based computing device, in
an artificially controlled environment (indoors) - thus a promising solution to promote
cognitive performance/development; and that interaction with the Biosymtic device
“Cratus”, in a natural environment, instilled vigorous physical activity levels in children
- thus a promising solution to promote physical and mental health
Affect Recognition in Autism: a single case study on integrating a humanoid robot in a standard therapy.
Autism Spectrum Disorder (ASD) is a multifaceted developmental disorder that comprises a mixture of social impairments, with deficits in many areas including the theory of mind, imitation, and communication. Moreover, people with autism have difficulty in recognising and understanding emotional expressions. We are currently working on integrating a humanoid robot within the standard clinical treatment offered to children with ASD to support the therapists. In this article, using the A-B-A' single case design, we propose a robot-assisted affect recognition training and to present the results on the child’s progress during the five months of clinical experimentation. In the investigation, we tested the generalization of learning and the long-term maintenance of new skills via the NEPSY-II affection recognition sub-test. The results of this single case study suggest the feasibility and effectiveness of using a humanoid robot to assist with emotion recognition training in children with ASD
Virtual Reality Games for Motor Rehabilitation
This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion
Interactive Imitation Learning in Robotics: A Survey
Interactive Imitation Learning (IIL) is a branch of Imitation Learning (IL)
where human feedback is provided intermittently during robot execution allowing
an online improvement of the robot's behavior. In recent years, IIL has
increasingly started to carve out its own space as a promising data-driven
alternative for solving complex robotic tasks. The advantages of IIL are its
data-efficient, as the human feedback guides the robot directly towards an
improved behavior, and its robustness, as the distribution mismatch between the
teacher and learner trajectories is minimized by providing feedback directly
over the learner's trajectories. Nevertheless, despite the opportunities that
IIL presents, its terminology, structure, and applicability are not clear nor
unified in the literature, slowing down its development and, therefore, the
research of innovative formulations and discoveries. In this article, we
attempt to facilitate research in IIL and lower entry barriers for new
practitioners by providing a survey of the field that unifies and structures
it. In addition, we aim to raise awareness of its potential, what has been
accomplished and what are still open research questions. We organize the most
relevant works in IIL in terms of human-robot interaction (i.e., types of
feedback), interfaces (i.e., means of providing feedback), learning (i.e.,
models learned from feedback and function approximators), user experience
(i.e., human perception about the learning process), applications, and
benchmarks. Furthermore, we analyze similarities and differences between IIL
and RL, providing a discussion on how the concepts offline, online, off-policy
and on-policy learning should be transferred to IIL from the RL literature. We
particularly focus on robotic applications in the real world and discuss their
implications, limitations, and promising future areas of research
INSPIRE Newsletter Spring 2022
https://scholarsmine.mst.edu/inspire-newsletters/1010/thumbnail.jp
AdaptiX -- A Transitional XR Framework for Development and Evaluation of Shared Control Applications in Assistive Robotics
With the ongoing efforts to empower people with mobility impairments and the
increase in technological acceptance by the general public, assistive
technologies, such as collaborative robotic arms, are gaining popularity. Yet,
their widespread success is limited by usability issues, specifically the
disparity between user input and software control along the autonomy continuum.
To address this, shared control concepts provide opportunities to combine the
targeted increase of user autonomy with a certain level of computer assistance.
This paper presents the free and open-source AdaptiX XR framework for
developing and evaluating shared control applications in a high-resolution
simulation environment. The initial framework consists of a simulated robotic
arm with an example scenario in Virtual Reality (VR), multiple standard control
interfaces, and a specialized recording/replay system. AdaptiX can easily be
extended for specific research needs, allowing Human-Robot Interaction (HRI)
researchers to rapidly design and test novel interaction methods, intervention
strategies, and multi-modal feedback techniques, without requiring an actual
physical robotic arm during the early phases of ideation, prototyping, and
evaluation. Also, a Robot Operating System (ROS) integration enables the
controlling of a real robotic arm in a PhysicalTwin approach without any
simulation-reality gap. Here, we review the capabilities and limitations of
AdaptiX in detail and present three bodies of research based on the framework.
AdaptiX can be accessed at https://adaptix.robot-research.de.Comment: Accepted submission at The 16th ACM SIGCHI Symposium on Engineering
Interactive Computing Systems (EICS'24
Augmented Reality and Robotics: A Survey and Taxonomy for AR-enhanced Human-Robot Interaction and Robotic Interfaces
This paper contributes to a taxonomy of augmented reality and robotics based on a survey of 460 research papers. Augmented and mixed reality (AR/MR) have emerged as a new way to enhance human-robot interaction (HRI) and robotic interfaces (e.g., actuated and shape-changing interfaces). Recently, an increasing number of studies in HCI, HRI, and robotics have demonstrated how AR enables better interactions between people and robots. However, often research remains focused on individual explorations and key design strategies, and research questions are rarely analyzed systematically. In this paper, we synthesize and categorize this research field in the following dimensions: 1) approaches to augmenting reality; 2) characteristics of robots; 3) purposes and benefits; 4) classification of presented information; 5) design components and strategies for visual augmentation; 6) interaction techniques and modalities; 7) application domains; and 8) evaluation strategies. We formulate key challenges and opportunities to guide and inform future research in AR and robotics
HUMAN ROBOT INTERACTION THROUGH SEMANTIC INTEGRATION OF MULTIPLE MODALITIES, DIALOG MANAGEMENT, AND CONTEXTS
The hypothesis for this research is that applying the Human Computer Interaction (HCI) concepts of using multiple modalities, dialog management, context, and semantics to Human Robot Interaction (HRI) will improve the performance of Instruction Based Learning (IBL) compared to only using speech. We tested the hypothesis by simulating a domestic robot that can be taught to clean a house using a multi-modal interface. We used a method of semantically integrating the inputs from multiple modalities and contexts that multiplies a confidence score for each input by a Fusion Weight, sums the products, and then uses the input with the highest product sum. We developed an algorithm for determining the Fusion Weights. We concluded that different modalities, contexts, and modes of dialog management impact human robot interaction; however, which combination is better depends on the importance of the accuracy of learning what is taught versus the succinctness of the dialog between the user and the robot
Assistive technology design and development for acceptable robotics companions for ageing years
© 2013 Farshid Amirabdollahian et al., licensee Versita Sp. z o. o. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs license, which means that the text may be used for non-commercial purposes, provided credit is given to the author.A new stream of research and development responds to changes in life expectancy across the world. It includes technologies which enhance well-being of individuals, specifically for older people. The ACCOMPANY project focuses on home companion technologies and issues surrounding technology development for assistive purposes. The project responds to some overlooked aspects of technology design, divided into multiple areas such as empathic and social human-robot interaction, robot learning and memory visualisation, and monitoring persons’ activities at home. To bring these aspects together, a dedicated task is identified to ensure technological integration of these multiple approaches on an existing robotic platform, Care-O-Bot®3 in the context of a smart-home environment utilising a multitude of sensor arrays. Formative and summative evaluation cycles are then used to assess the emerging prototype towards identifying acceptable behaviours and roles for the robot, for example role as a butler or a trainer, while also comparing user requirements to achieved progress. In a novel approach, the project considers ethical concerns and by highlighting principles such as autonomy, independence, enablement, safety and privacy, it embarks on providing a discussion medium where user views on these principles and the existing tension between some of these principles, for example tension between privacy and autonomy over safety, can be captured and considered in design cycles and throughout project developmentsPeer reviewe
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