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Title: Workspace Aware Online Grasp Planning
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 feasibility. 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.
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Award ID(s):
Publication Date:
Journal Name:
Proceedings Article published Oct 2018 in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Page Range or eLocation-ID:
2917 to 2924
Sponsoring Org:
National Science Foundation
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