Bimanual Deformable Bag Manipulation Using a Structure-of-Interest Based Neural Dynamics Model

TL;DR

This paper presents a novel bimanual manipulation framework for deformable fabric bags using a Graph Neural Network-based latent dynamics model focused on Structures of Interest, enabling precise control and manipulation of complex deformable objects.

Contribution

It introduces a GNN-based latent dynamics model for deformable object manipulation, emphasizing Structures of Interest to improve robotic control of fabric bags.

Findings

Effective manipulation of fabric bags demonstrated in experiments.

Latent dynamics model accurately predicts SOI deformations.

Framework enhances stability and precision in DOM tasks.

Abstract

The manipulation of deformable objects by robotic systems presents a significant challenge 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 empower robotic manipulators to perform precise and stable manipulation tasks focused on the SOIs. We have validated our framework through various empirical experiments demonstrating its efficacy in bimanual manipulation of fabric bags. Our contributions not only address the complexities inherent in DOM but also provide new perspectives and methodologies for enhancing robotic interactions with deformable objects by concentrating on their critical structural elements. Experimental videos can be obtained from https://sites.google.com/view/bagbot.