Researcher:
Ünal, Emre

Profile Picture
ORCID

Job Title

Other

First Name

Emre

Last Name

Ünal

Name

Name Variants

Ünal, Emre

Email Address

Birth Date

Filters

20192

Advanced Search

Filter by

Settings

Researcher Of Publications: search.filters.isAuthorOfPublication.678a42cb-192d-4e9e-a67c-079901c6b6a4

Search Results

Now showing 1 - 2 of 2
  • Placeholder
    PublicationMetadata only
    Learning to follow verbal instructions with visual grounding
    (Institute of Electrical and Electronics Engineers (IEEE), 2019) Department of Electrical and Electronics Engineering; N/A; Department of Computer Engineering; Ünal, Emre; Can, Ozan Arkan; Yemez, Yücel; Other; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 107907
    We 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.
  • Thumbnail Image
    PublicationOpen Access
    Visually grounded language learning for robot navigation
    (Association for Computing Machinery (ACM), 2019) Department of Computer Engineering; Yemez, Yücel; Ünal, Emre; Can, Ozan Arkan; Faculty Member; Other; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and Engineering; 107907; N/A; N/A
    We present an end-to-end deep learning model for robot navigation from raw visual pixel input and natural text instructions. The proposed model is an LSTM-based sequence-to-sequence neural network architecture with attention, which is trained on instructionperception data samples collected in a synthetic environment. We conduct experiments on the SAIL dataset which we reconstruct in 3D so as to generate the 2D images associated with the data. Our experiments show that the performance of our model is on a par with state-of-the-art, despite the fact that it learns navigational language with end-to-end training from raw visual data.