Department of Electrical and Electronics EngineeringDepartment of Computer Engineering2024-11-0920199781-7281-1904-510.1109/SIU.2019.88063352-s2.0-85071986881http://dx.doi.org/10.1109/SIU.2019.8806335https://hdl.handle.net/20.500.14288/15624We present a visually grounded deep learning model towards a virtual robot that can follow navigational instructions. Our model is capable of processing raw visual input and natural text instructions. The aim is to develop a model that can learn to follow novel instructions from instruction-perception examples. The proposed model is trained on data collected in a synthetic environment and its architecture allows it to work also with real visual data. We show that our results are on par with the previously proposed methods.Civil engineeringElectrical electronics engineeringTelecommunicationConference proceedinghttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85071986881&doi=10.1109%2fSIU.2019.8806335&partnerID=40&md5=581b5b694b4b4cfcc30d3fb34e5067fa5189943000618839