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

N. Passalis, S. Pedrazzi, R. Babuska, W. Burgard, F. Ferro, M. Gabbouj, E. Kayacan, J. Kober, R. Pieters, A. Valada, More Authors

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
11 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 publicationProceedings 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PublisherIEEE
Pages12479-12484
ISBN (Print)978-1-6654-7927-1
DOIs
Publication statusPublished - 2022
EventThe 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022): IROS 2022 - Kyoto, Japan
Duration: 23 Oct 202227 Oct 2022

Conference

ConferenceThe 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)
Abbreviated titleIROS 2022
Country/TerritoryJapan
CityKyoto
Period23/10/2227/10/22

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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