Task Learning for Intention Detection using Deep Neural Networks and Robotic Arm Data in Glovebox

Abdullah Alharthi, Ozan Tokatli, Erwin Jose Lopez Pulgarin, Guido Herrmann

Research output: Contribution to conferencePoster

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Abstract

Tele-manipulation systems are becoming more reliant on complex local (master) devices with sophisticated control methods; hence, the cognitive load on the operator during labour intensive tasks is increasing. The operator intention detection based on task learning can lead to better robot task performance with less human effort in teleoperation for a glovebox environment (see Fig. 1). Deep Convolutional Neural Networks are proposed to learn and predict the operator intention using robotic arm and its controller spatiotemporal data. Our preliminary experimental study on glovebox tasks for nuclear applications, particularly radiation survey and object grasping, provided promising results and encouraged us for a deeper research.
Original languageEnglish
Pages1
Number of pages1
DOIs
Publication statusPublished - 16 Mar 2022
EventROBOTICS AND AI IN NUCLEAR (RAIN Hub): Project Demonstration - Culham science centre, UK Atomic Energy Authority. , Oxford , United Kingdom
Duration: 16 Mar 202216 Mar 2022
https://rainhub.org.uk/

Other

OtherROBOTICS AND AI IN NUCLEAR (RAIN Hub)
Country/TerritoryUnited Kingdom
CityOxford
Period16/03/2216/03/22
Internet address

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