2026
Learning From Planned Data to Improve Robotic Pick-and-Place Planning Efficiency
RA-L 2026
This work proposes a learning method to accelerate robotic pick-and-place planning by predicting shared grasps. Shared grasps are defined as grasp poses feasible to both the initial and goal object configurations in a pick-and-place task. Traditional analytical methods for solving shared grasps eval