# Regrasp Planning using 10,000s of Grasps

## TL;DR

This paper introduces advanced regrasp planning algorithms that utilize tens of thousands of grasps and relational databases, significantly improving robustness and efficiency in robotic object reorientation tasks.

## Contribution

It presents novel algorithms that leverage over-segmented meshes, relational databases, and real-world roadmaps for large-scale, robust regrasp planning in robotics.

## Key findings

- Robust regrasp planning with 10,000+ grasps achieved.
- Algorithms validated on various objects and robots.
- Interactive planning time demonstrated.

## Abstract

This paper develops intelligent algorithms for robots to reorient objects. Given the initial and goal poses of an object, the proposed algorithms plan a sequence of robot poses and grasp configurations that reorient the object from its initial pose to the goal. While the topic has been studied extensively in previous work, this paper makes important improvements in grasp planning by using over-segmented meshes, in data storage by using relational database, and in regrasp planning by mixing real-world roadmaps. The improvements enable robots to do robust regrasp planning using 10,000s of grasps and their relationships in interactive time. The proposed algorithms are validated using various objects and robots.