Goal-Conditioned Model Simplification for Deformable Object Manipulation

Abstract

Planning for deformable object manipulation has been a challenge for a long time in robotics due to its computational cost. In this work, we propose to reduce this computational cost by reducing the number of pick points on a deformable object. 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 particles through a model simplification process, which finds the minimal model that still enables a good approximation of the original model at the goal state. We present an implementation of this general approach for 1-D linear deformable objects (e.g., ropes) that uses a piece- wise linear simplified model, and for 2-D flat deformable objects (e.g., cloth) that uses a mesh simplified model. We conduct simulated experiments on ropes and cloths, which demonstrate the effectiveness of the proposed method. Finally, the planned paths are executed in a real world setting for two cloth folding tasks.