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

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.