Passalis, Nikolaos and Pedrazzi, Stefania and Babuska, Robert and Burgard, Wolfram and Dias, Daniel and Ferro, Francesco and Gabbouj, Moncef and Ole, Green and Iosifidis, Alexandros and Kayacan, Erdal and Kober, Jens and Michel, Olivier and Nikolaidis, Nikolaos and Nousi, Paraskevi and Pieters, Roel S. and Tzelepi, Maria and Valada, Abhinav and Tefas, Anastasios, OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint Deep Learning for Robotics , 2022. [PDF] [Website]
Open Deep Learning Toolkit for Robotics (OpenDR)
Abstract: The aim of OpenDR is to develop a modular, open and non-proprietary deep learning toolkit for robotics. We will provide a set of software functions, packages and utilities to help roboticists develop and test robotic applications that incorporate deep learning. OpenDR will enable linking robotics applications to software libraries such as tensorflow and the ROS operating environment. We focus on the AI and cognition core technology in order to give robotic systems the ability to interact with people and environments by means of deep-learning methods for active perception, cognition and decisions making. OpenDR will enlarge the range of robotics applications making use of deep learning, which will be demonstrated in the applications areas of healthcare, agri-food and agile production.
Project Type: EU Horizon 2020 program, call H2020-ICT-2018-2020 (Information and Communication Technologies); 2019 – 2022
Consortium: Aristotle University of Thessaloniki, Tampere University of Technology, Aarhus University, Delft University of Technology, Albert-Ludwigs-Universität Freiburg, Cyberbotics Ltd., PAL Robotics S.L., Agro Intelligence ApS
Members: ir. Bas van der Heijden, Dr. Osama Mazhar, Dr. Laura Ferranti , Dr.-Ing. Jens Kober , prof.dr. Robert Babuška
Publications
Mazhar, Osama and Kober, Jens, Random Shadows and Highlights: A New Data Augmentation Method for Extreme Lighting Conditions , 2021. [PDF] [Code] [Website]
Mazhar, Osama and Ramdani, Sofiane and Cherubini, Andrea, A Deep Learning Framework for Recognizing Both Static and Dynamic Gestures , Sensors, 21 (6), 2021. [DOI] [PDF] [Video]
Mazhar, Osama and Babuska, Robert and Kober, Jens, GEM: Glare or Gloom, I Can Still See You – End-to-End Multimodal Object Detector , IEEE Robotics and Automation Letters, 6 (4), 6321--6328, 2021. [DOI] [PDF]
van der Heijden, Bas and Ferranti, Laura and Kober, Jens and Babuska, Robert, DeepKoCo: Efficient Latent Planning with an Invariant Koopman Representation , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 183--189, 2021. [DOI] [PDF] [Website]