Towards Object Agnostic and Robust 4-DoF Table-Top Grasping

Abstract

A fully automated and reliable picking of a diverse range of previously unseen objects in clutter is a challenging problem. This becomes even more difficult given the inherent uncertainty in sensing, control, and interaction physics. This paper presents a robust method for stable and collision-free grasp planning, given a cluttered heap of novel objects of different varieties. Our grasp planning pipeline leverages a novel grasp pose ranking method and a pose refinement method that ensures collision-free gripping and stable contact between gripper-fingers and the target object. Often, a grasp planning algorithm may not be able to find a valid grasp pose due to the tightly-packed configuration of the objects. In such situations, our method directs the robot to perform a clutter removal action using a linear push policy. On a physical robot with a two-fingered parallel-jaw gripper and a depth sensor, our method can consistently clear up the pile of up to 20 objects with 95% reliability.