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Heterogeneous systems manipulation, i.e., manipulating rigid objects viadeformable (soft) objects, is an emerging field that remains in its earlystages of research. Existing works in this field suffer from limited action andoperational space, poor generalization ability, and expensive development. Toaddress these challenges, we propose a universally applicable and effectivemoving primitive, Iterative Grasp-Pull (IGP), and a sample-based framework,DeRi-IGP, to solve the heterogeneous system manipulation task. The DeRi-IGPframework uses local onboard robots' RGBD sensors to observe the environment,comprising a soft-rigid body system. It then uses this information toiteratively grasp and pull a soft body (e.g., rope) to move the attached rigidbody to a desired location. We evaluate the effectiveness of our framework insolving various heterogeneous manipulation tasks and compare its performancewith several state-of-the-art baselines. The result shows that DeRi-IGPoutperforms other methods by a significant margin. In addition, we alsodemonstrate the advantage of the large operational space of IGP in thelong-distance object acquisition task within both simulated and realenvironments.

DeRi-IGP: Manipulating Rigid Objects Using Deformable Objects via Iterative Grasp-Pull