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Garbage Collection and Sorting with a Mobile Manipulator using Deep Learning and Whole-Body Control

Liu, J; Balatti, P; Ellis, K; Hadjivelichkov, D; Stoyanov, D; Ajoudani, A; Kanoulas, D; (2021) Garbage Collection and Sorting with a Mobile Manipulator using Deep Learning and Whole-Body Control. In: Proceedings of the 2020 IEEE-RAS 20th International Conference on Humanoid Robots (Humanoids). (pp. pp. 408-414). Institute of Electrical and Electronics Engineers (IEEE): Munich, Germany. Green open access

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Abstract

Domestic garbage management is an important aspect of a sustainable environment. This paper presents a novel garbage classification and localization system for grasping and placement in the correct recycling bin, integrated on a mobile manipulator. In particular, we first introduce and train a deep neural network (namely, GarbageNet) to detect different recyclable types of garbage. Secondly, we use a grasp localization method to identify a suitable grasp pose to pick the garbage from the ground. Finally, we perform grasping and sorting of the objects by the mobile robot through a whole-body control framework. We experimentally validate the method, both on visual RGB-D data and indoors on a real full-size mobile manipulator for collection and recycling of garbage items placed on the ground.

Type: Proceedings paper
Title: Garbage Collection and Sorting with a Mobile Manipulator using Deep Learning and Whole-Body Control
Event: 2020 IEEE-RAS 20th International Conference on Humanoid Robots (Humanoids)
ISBN-13: 978-1-7281-9372-4
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/HUMANOIDS47582.2021.9555800
Publisher version: https://doi.org/10.1109/HUMANOIDS47582.2021.955580...
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10133187
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