Workspace Aware Online Grasp Planning
This work addresses grasp planning for robotics by making it more efficient and effective, though it is incremental as it builds on standard methods.
The paper tackles the problem of online grasp planning by incorporating reachability constraints to improve performance, resulting in a higher percentage of reachable and successful grasps with reduced planning time.
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.