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UKAEA-RACE-PR(24)052024
In current telerobotics and telemanipulator applications, operators must perform a wide variety of tasks, often with a high risk associated with failure. A system designed to generate data-based behavioural estimations using observed operator features could be used to reduce risks in industrial teleoperation. This paper describes a non-invasive …
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UKAEA-RACE-PR(24)032024
The use of robots has exceeded the standard focus of manufacturing and production. Over the last decades, special robotic systems have been developed in various extreme environments, such as in the maintenance, repair or even decommissioning of large-scale, strategic facilities, important to any nation’s infrastructure, including power, space,…
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UKAEA-RACE-CP(23)062023
Sim-and-real training is a promising alternative to sim-to-real training for robot manipulations. However, the current sim-and-real training is neither efficient, i.e., slow convergence to the optimal policy, nor effective, i.e., sizeable real-world robot data. Given limited time and hardware budgets, the performance of sim-and-real training is …
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UKAEA-CCFE-PR(22)292022
Tele-manipulation is indispensable for the nuclear industry, since teleoperated robots cancel the radiation hazard problem for the operator. However, the performance limitations of teleoperated robots for nuclear decommissioning tasks is not clearly answered in the literature. In this paper, we propose a task performance-based methodology to evalua…
Showing 1 - 4 of 4 UKAEA Paper Results
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