Grasping for a Purpose: Using Task Goals for Efficient Manipulation Planning
This work addresses the challenge of reducing planning time for robot manipulation tasks, though it is incremental as it builds on existing grasp selection methods.
The paper tackles the problem of efficient grasp selection for manipulation tasks by proposing grasp manipulability as a metric to prioritize candidate grasps, demonstrating its usefulness in simulation and physical robot experiments with household objects.
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.