Single and Multiple View Support Order Prediction in Clutter for Manipulation

Abstract

Robotic manipulation of objects in clutter remains a challenging problem to date. The challenge is posed by various levels of complexity involved in interaction among objects. Understanding these semantic interactions among different objects is important to manipulate in complex settings. It can play a significant role in extending the scope of manipulation to cluttered environment involving generic objects, and both direct and indirect physical contact. In our work, we aim at learning semantic interaction among objects of generic shapes and sizes lying in clutter involving physical contact. We infer three types of support relationships: "support from below", "support from side", and "containment". Subsequently, the learned semantic interaction or support relationship is used to derive a sequence or order in which the objects surrounding the object of interest should be removed without causing damage to the environment. The generated sequence is called support order. We also extend understanding of semantic interaction from single view to multiple views and predict support order in multiple views. Using multiple views addresses those cases that are not handled when using single view such as scenarios of occlusion or missing support relationships. We have created two RGBD datasets for our experiments on support order prediction in single view and multiple views respectively. The datasets contains RGB images, point clouds and depth maps of various objects used in day-to-day life present in clutter with physical contact and overlap. We captured many different cluttered settings involving different kinds of object-object interaction and successfully learned support relationship and performed Support Order Prediction in these settings.