Goal-Conditioned Action Space Reduction for Deformable Object Manipulation
Shengyin Wang,Rafael Papallas,Matteo Leonetti,Mehmet Dogar,Shengyin Wang,Rafael Papallas,Matteo Leonetti,Mehmet Dogar
Planning for deformable object manipulation has been a challenge for a long time in robotics due to its high computational cost. In this work, we propose to reduce this cost by reducing the number of pick points on a deformable object in the action space. We do this by identifying a small number of key particles that are sufficient as pick points to reach a given goal state. We find these key part...
