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.
@article{arxiv.1806.11402,
title = {Workspace Aware Online Grasp Planning},
author = {Iretiayo Akinola and Jacob Varley and Boyuan Chen and Peter K. Allen},
journal= {arXiv preprint arXiv:1806.11402},
year = {2018}
}