Grasp selection analysis for two-step manipulation tasks

TL;DR

This paper analyzes grasp selection strategies for two-step manipulation tasks, demonstrating how to effectively use a manipulation metric at different task stages to improve grasp planning in both simulated and real robot experiments.

Contribution

It introduces a method to utilize a manipulation metric for grasp ranking in two-step tasks, clarifying whether to consider start, goal, or combined states based on task constraints.

Findings

Combining start and goal metrics improves grasp selection in constrained tasks.

Start state metrics are preferable for less constrained tasks.

Validated approach with both simulation and physical robot experiments.

Abstract

Manipulation tasks are sequential in nature. Grasp selection approaches that take into account the con- straints at each task step are critical, since they allow to both (1) Identify grasps that likely require simple arm motions through the whole task and (2) To discard grasps that, although feasible to achieve at earlier steps, might not be executable at later stages due to goal task constraints. In this paper, we study how to use our previously proposed manipulation metric for tasks in which 2 steps are required (pick-and-place and pouring tasks). Even for such simple tasks, it was not clear how to use the results of applying our metric (or any metric for that matter) to rank all the candidate grasps: Should only the start state be considered, or only the goal, or a combination of both? In order to find an answer, we evaluated the (best) grasps selected by our metric under each of these 3 considerations. Our main conclusion was that for tasks in which the goal state is more constrained (pick-and-place), using a combination of the metric measured at the start and goal states renders better performance when compared with choosing any other candidate grasp, whereas in tasks in which the goal constraints are less rigidly defined, the metric measured at the start state should be mainly considered. We present quantitative results in simulation and validate our approach's practicality with experimental results in our physical robot manipulator, Crichton.