Bimanual Manipulation of Deformable Bags Using Structure of Interest-based Latent Dynamics Model

The manipulation of deformable objects by robotic systems presents significant challenges due to their complex and infinite-dimensional configuration spaces. This paper introduces a novel approach to deformable object manipulation (DOM) by emphasizing the identification and manipulation of structures of interest (SOIs) in deformable fabric bags. We propose a bimanual manipulation framework that leverages a graph neural network (GNN)-based latent dynamics model to succinctly represent and predict the behavior of these SOIs. Our approach involves constructing a graph representation from partial point cloud data of the object and learning the latent dynamics model that effectively captures the essential deformations of the fabric bag within a reduced computational space. By integrating this latent dynamics model with model predictive control (MPC), we enable robotic manipulators to perform precise and stable manipulation tasks focused on the SOIs. We validate our new framework through various experiments that demonstrate its efficacy in manipulating deformable bags. Our contributions not only address the complexities inherent in DOM but also provide new perspectives and methodologies for enhancing robotic interactions with deformable materials by concentrating on their critical structural elements.