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      Toward Enhanced Teleoperation Through Embodiment

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          Abstract

          Telerobotics aims to transfer human manipulation skills and dexterity over an arbitrary distance and at an arbitrary scale to a remote workplace. A telerobotic system that is transparent enables a natural and intuitive interaction. We postulate that embodiment (with three sub-components: sense of ownership, agency, and self-location) of the robotic system leads to optimal perceptual transparency and increases task performance. However, this has not yet been investigated directly. We reason along four premises and present findings from the literature that substantiate each of them: (1) the brain can embody non-bodily objects (e.g., robotic hands), (2) embodiment can be elicited with mediated sensorimotor interaction, (3) embodiment is robust against inconsistencies between the robotic system and the operator's body, and (4) embodiment positively correlates to dexterous task performance. We use the predictive encoding theory as a framework to interpret and discuss the results reported in the literature. Numerous previous studies have shown that it is possible to induce embodiment over a wide range of virtual and real extracorporeal objects (including artificial limbs, avatars, and android robots) through mediated sensorimotor interaction. Also, embodiment can occur for non-human morphologies including for elongated arms and a tail. In accordance with the predictive encoding theory, none of the sensory modalities is critical in establishing ownership, and discrepancies in multisensory signals do not necessarily lead to loss of embodiment. However, large discrepancies in terms of multisensory synchrony or visual likeness can prohibit embodiment from occurring. The literature provides less extensive support for the link between embodiment and (dexterous) task performance. However, data gathered with prosthetic hands do indicate a positive correlation. We conclude that all four premises are supported by direct or indirect evidence in the literature, suggesting that embodiment of a remote manipulator may improve dexterous performance in telerobotics. This warrants further implementation testing of embodiment in telerobotics. We formulate a first set of guidelines to apply embodiment in telerobotics and identify some important research topics.

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          Most cited references251

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          Rubber hands 'feel' touch that eyes see.

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            Defining Virtual Reality: Dimensions Determining Telepresence

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              Principles of sensorimotor learning.

              The exploits of Martina Navratilova and Roger Federer represent the pinnacle of motor learning. However, when considering the range and complexity of the processes that are involved in motor learning, even the mere mortals among us exhibit abilities that are impressive. We exercise these abilities when taking up new activities - whether it is snowboarding or ballroom dancing - but also engage in substantial motor learning on a daily basis as we adapt to changes in our environment, manipulate new objects and refine existing skills. Here we review recent research in human motor learning with an emphasis on the computational mechanisms that are involved.
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                Author and article information

                Contributors
                Journal
                Front Robot AI
                Front Robot AI
                Front. Robot. AI
                Frontiers in Robotics and AI
                Frontiers Media S.A.
                2296-9144
                11 February 2020
                2020
                : 7
                : 14
                Affiliations
                [1] 1Perceptual and Cognitive Systems, Netherlands Organisation for Applied Scientific Research (TNO) , Soesterberg, Netherlands
                [2] 2Intelligent Autonomous Systems, Netherlands Organisation for Applied Scientific Research (TNO) , The Hague, Netherlands
                [3] 3Human Media Interaction, University of Twente , Enschede, Netherlands
                Author notes

                Edited by: Mel Slater, University of Barcelona, Spain

                Reviewed by: Hidenobu Sumioka, Advanced Telecommunications Research Institute International (ATR), Japan; Sameer Kishore, Middlesex University Dubai, United Arab Emirates

                *Correspondence: Alexander Toet lex.toet@ 123456tno.nl

                This article was submitted to Virtual Environments, a section of the journal Frontiers in Robotics and AI

                Article
                10.3389/frobt.2020.00014
                7805894
                33501183
                c6b74450-90b9-4966-9274-958b18e62957
                Copyright © 2020 Toet, Kuling, Krom and van Erp.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 02 August 2019
                : 21 January 2020
                Page count
                Figures: 0, Tables: 2, Equations: 0, References: 251, Pages: 22, Words: 21506
                Categories
                Robotics and AI
                Hypothesis and Theory

                teleoperation,body ownership illusion,rubber hand illusion,performance enhancement,embodiment,robotics,telemanipulation,robotic dexterity

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