Conference paper Open Access

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

Jingyi Liu; Pietro Balatti; Kirsty Ellis; Denis Hadjivelichkov; Danail Stoyanov; Arash Ajoudani; Dimitrios Kanoulas

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.

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