DeRi-IGP: Learning to Manipulate Rigid Objects Using Deformable Linear Objects via Iterative Grasp-Pull

Robotic manipulation of rigid objects via deformable linear objects (DLO) such as ropes is an emerging field of research with applications in various rigid object transportation tasks. A few methods that exist in this field suffer from limited robot action and operational space, poor generalization ability, and expensive model-based development. To address these challenges, we propose a universally applicable moving primitive called Iterative Grasp-Pull (IGP). We also introduce a novel vision-based neural policy that learns to parameterize the IGP primitive to manipulate DLO and transport their attached rigid objects to the desired goal locations. Additionally, our decentralized algorithm design allows collaboration among multiple agents to manipulate rigid objects using DLO. We evaluated the effectiveness of our approach in both simulated and real-world environments for a variety of soft-rigid body manipulation tasks. In the real world, we also demonstrate the effectiveness of our decentralized approach through human-robot collaborative transportation of rigid objects to given goal locations. We also showcase the large operational space of IGP primitive by solving distant object acquisition tasks. Lastly, we compared our approach with several model-based and learning-based baseline methods. The results indicate that our method surpasses other approaches by a significant margin.

I. Introduction

The heterogeneous system manipulation task requires one or more robots to manipulate a rigid object via the connected deformable (soft) objects [1]. Such configuration applies to various scenarios, such as transporting payloads with cargo sleds in snowy terrains and hauling tree chunks using ropes in forestry. The soft bodies in these systems provide enhanced maneuverability and portability because of their high degree of freedom. Consequently, we believe it is important to develop frameworks that can operate robots under such a heterogeneous configuration, leading to improvements in their capabilities and expanded application across a wide range of object transportation tasks.