Workspace Aware Online Grasp Planning

TL;DR

This paper introduces a workspace-aware online grasp planning framework that leverages a large offline database to bias grasp configurations towards reachability, improving success rates and reducing planning time in robotic grasping tasks.

Contribution

The novel framework integrates reachability information into online grasp planning, significantly enhancing performance over standard methods.

Findings

Higher percentage of reachable grasps

Increased grasp success rate

Reduced planning time

Abstract

This work provides a framework for a workspace aware online grasp planner. This framework greatly improves the performance of standard online grasp planning algorithms by incorporating a notion of reachability into the online grasp planning process. Offline, a database of hundreds of thousands of unique end-effector poses were queried for feasability. At runtime, our grasp planner uses this database to bias the hand towards reachable end-effector configurations. The bias keeps the grasp planner in accessible regions of the planning scene so that the resulting grasps are tailored to the situation at hand. This results in a higher percentage of reachable grasps, a higher percentage of successful grasp executions, and a reduced planning time. We also present experimental results using simulated and real environments.