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Recent research efforts have yielded significant advancements in manipulatingobjects under homogeneous settings where the robot is required to eithermanipulate rigid or deformable (soft) objects. However, the manipulation underheterogeneous setups that involve both rigid and one-dimensional (1D)deformable objects remains an unexplored area of research. Such setups arecommon in various scenarios that involve the transportation of heavy objectsvia ropes, e.g., on factory floors, at disaster sites, and in forestry. Toaddress this challenge, we introduce DeRi-Bot, the first framework that enablesthe collaborative manipulation of rigid objects with deformable objects. Ourframework comprises an Action Prediction Network (APN) and a ConfigurationPrediction Network (CPN) to model the complex pattern and stochasticity ofsoft-rigid body systems. We demonstrate the effectiveness of DeRi-Bot in movingrigid objects to a target position with ropes connected to robotic arms.Furthermore, DeRi-Bot is a distributive method that can accommodate anarbitrary number of robots or human partners without reconfiguration orretraining. We evaluate our framework in both simulated and real-worldenvironments and show that it achieves promising results with stronggeneralization across different types of objects and multi-agent settings,including human-robot collaboration.

DeRi-Bot: Learning to Collaboratively Manipulate Rigid Objects via Deformable Objects