Grasping for a Purpose: Using Task Goals for Efficient Manipulation Planning

TL;DR

This paper introduces a method for efficient grasp selection in manipulation tasks by prioritizing candidate grasps using a manipulability metric, demonstrated through simulation and physical robot experiments.

Contribution

It proposes using grasp manipulability as a metric to order candidate grasps, improving efficiency in manipulation planning for unknown objects.

Findings

Simulation experiments show the effectiveness of the manipulability metric.

Physical robot experiments validate the approach on household objects.

The method reduces the number of grasp tests needed for successful manipulation.

Abstract

In this paper we propose an approach for efficient grasp selection for manipulation tasks of unknown objects. Even for simple tasks such as pick-and-place, a unique solution is rare to occur. Rather, multiple candidate grasps must be considered and (potentially) tested till a successful, kinematically feasible path is found. To make this process efficient, the grasps should be ordered such that those more likely to succeed are tested first. We propose to use grasp manipulability as a metric to prioritize grasps. We present results of simulation experiments which demonstrate the usefulness of our metric. Additionally, we present experiments with our physical robot performing simple manipulation tasks with a small set of different household objects.