Shape Based Learning for Grasping Novel Objects in Cluttered Scenes

This paper presents a novel approach to clearing a table with a heap of objects. Form, size, position, orientation and constellation of the objects are a priori unknown. Coping with incomplete point cloud data is an additional challenge. There are three key contributions. First, we introduce Height Accumulated Features (HAF) which provide an efficient way of calculating grasp related feature values. The second contribution is an extensible machine learning system for binary classification of grasp hypotheses based on raw point cloud data. Finally, a practical heuristic for selection of the most robust grasp hypothesis is introduced. We evaluate our system in experiments where a robot was required to autonomously clear a table with a heap of unknown objects. Despite the complexity of the scenarios, our system cleared the table each time without human interaction and with a grasp failure rate below three percent.