  Heterogeneous systems manipulation, i.e., manipulating rigid objects via
deformable (soft) objects, is an emerging field that remains in its early
stages of research. Existing works in this field suffer from limited action and
operational space, poor generalization ability, and expensive development. To
address these challenges, we propose a universally applicable and effective
moving primitive, Iterative Grasp-Pull (IGP), and a sample-based framework,
DeRi-IGP, to solve the heterogeneous system manipulation task. The DeRi-IGP
framework uses local onboard robots' RGBD sensors to observe the environment,
comprising a soft-rigid body system. It then uses this information to
iteratively grasp and pull a soft body (e.g., rope) to move the attached rigid
body to a desired location. We evaluate the effectiveness of our framework in
solving various heterogeneous manipulation tasks and compare its performance
with several state-of-the-art baselines. The result shows that DeRi-IGP
outperforms other methods by a significant margin. We also evaluate the
sim-to-real generalization of our framework through real-world human-robot
collaborative goal-reaching and distant object acquisition tasks. Our framework
successfully transfers to the real world and demonstrates the advantage of the
large operational space of the IGP primitive.
