6815122
doi
10.1109/HUMANOIDS47582.2021.9555800
oai:zenodo.org:6815122
user-h2020-sophia
user-eu
Pietro Balatti
Istituto Italiano di Tecnologia
Kirsty Ellis
University College London
Denis Hadjivelichkov
University College London
Danail Stoyanov
University College London
Arash Ajoudani
Istituto Italiano di Tecnologia
Dimitrios Kanoulas
University College London
Garbage Collection and Sorting with a Mobile Manipulator using Deep Learning and Whole-Body Control
Jingyi Liu
University College London
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Mobile manipulation
<p>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.</p>
Zenodo
2021-10-11
info:eu-repo/semantics/conferencePaper
6815121
user-h2020-sophia
user-eu
award_title=Socio-physical Interaction Skills for Cooperative Human-Robot Systems in Agile Production; award_number=871237; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/871237; funder_id=00k4n6c32; funder_name=European Commission;
1657890794.334461
12354690
md5:2d04c20def53a46763428a638770fda2
https://zenodo.org/records/6815122/files/humanoids_2020_jingyi.pdf
public