Synthesizing Grasps and Regrasps for Complex Manipulation Tasks

TL;DR

This paper introduces a novel algorithm for planning and executing grasp and regrasp sequences for complex manipulation tasks using point cloud data, enabling robots to handle objects with changing path constraints.

Contribution

It formalizes the grasp and regrasp problem for complex tasks with point cloud data and proposes an algorithm based on screw motions and grasp metrics.

Findings

Successfully applied to RGB-D sensor data

Demonstrates effective regrasp planning for complex paths

Improves manipulation capabilities beyond simple pick-and-place

Abstract

In complex manipulation tasks, e.g., manipulation by pivoting, the motion of the object being manipulated has to satisfy path constraints that can change during the motion. Therefore, a single grasp may not be sufficient for the entire path, and the object may need to be regrasped. Additionally, geometric data for objects from a sensor are usually available in the form of point clouds. The problem of computing grasps and regrasps from point-cloud representation of objects for complex manipulation tasks is a key problem in endowing robots with manipulation capabilities beyond pick-and-place. In this paper, we formalize the problem of grasping/regrasping for complex manipulation tasks with objects represented by (partial) point clouds and present an algorithm to solve it. We represent a complex manipulation task as a sequence of constant screw motions. Using a manipulation plan skeleton as a sequence of constant screw motions, we use a grasp metric to find graspable regions on the object for every constant screw segment. The overlap of the graspable regions for contiguous screws are then used to determine when and how many times the object needs to be regrasped. We present experimental results on point cloud data collected from RGB-D sensors to illustrate our approach.