OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint Deep Learning for Robotics

N. Passalis, S. Pedrazzi, R. Babuska, W. Burgard, D. Dias, F. Ferro, M. Gabbouj, O. Green, A. Iosifidis, E. Kayacan, J. Kober, O. Michel, N. Nikolaidis, P. Nousi, R. Pieters, M. Tzelepi, A. Valada, A. Tefas

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Downloads (Pure)

Abstract

Existing Deep Learning (DL) frameworks typically do not provide ready-to-use solutions for robotics, where very specific learning, reasoning, and embodiment problems exist. Their relatively steep learning curve and the different methodologies employed by DL compared to traditional approaches, along with the high complexity of DL models, which often leads to the need of employing specialized hardware accelerators, further increase the effort and cost needed to employ DL models in robotics. Also, most of the existing DL methods follow a static inference paradigm, as inherited by the traditional computer vision pipelines, ignoring active perception, which can be employed to actively interact with the environment in order to increase perception accuracy. In this paper, we present the Open Deep Learning Toolkit for Robotics (OpenDR). OpenDR aims at developing an open, non-proprietary, efficient, and modular toolkit that can be easily used by robotics companies and research institutions to efficiently develop and deploy AI and cognition technologies to robotics applications, providing a solid step towards addressing the aforementioned challenges. We also detail the design choices, along with an abstract interface that was created to overcome these challenges. This interface can describe various robotic tasks, spanning beyond traditional DL cognition and inference, as known by existing frameworks, incorporating openness, homogeneity and robotics-oriented perception e.g., through active perception, as its core design principles.
Original languageEnglish
Title of host publication2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PublisherIEEE
Pages12479-12484
Number of pages6
ISBN (Electronic)978-1-6654-7927-1
DOIs
Publication statusPublished - 2022
Publication typeA4 Article in conference proceedings
EventIEEE/RSJ International Conference on Intelligent Robots and Systems - Kyoto, Japan
Duration: 23 Oct 202227 Oct 2022

Conference

ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems
Country/TerritoryJapan
CityKyoto
Period23/10/2227/10/22

Keywords

  • Deep learning
  • Computational modeling
  • Pipelines
  • Active perception
  • Solids
  • Cognition
  • Task analysis

Publication forum classification

  • Publication forum level 1

Fingerprint

Dive into the research topics of 'OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint Deep Learning for Robotics'. Together they form a unique fingerprint.

Cite this