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Deformable linear objects (DLOs), such as rods, cables, and ropes, playimportant roles in daily life. However, manipulation of DLOs is challenging aslarge geometrically nonlinear deformations may occur during the manipulationprocess. This problem is made even more difficult as the different deformationmodes (e.g., stretching, bending, and twisting) may result in elasticinstabilities during manipulation. In this paper, we formulate a physics-guideddata-driven method to solve a challenging manipulation task -- accuratelydeploying a DLO (an elastic rod) onto a rigid substrate along variousprescribed patterns. Our framework combines machine learning, scaling analysis,and physical simulations to develop a physics-based neural controller fordeployment. We explore the complex interplay between the gravitational andelastic energies of the manipulated DLO and obtain a control method for DLOdeployment that is robust against friction and material properties. Out of thenumerous geometrical and material properties of the rod and substrate, we showthat only three non-dimensional parameters are needed to describe thedeployment process with physical analysis. Therefore, the essence of thecontrolling law for the manipulation task can be constructed with alow-dimensional model, drastically increasing the computation speed. Theeffectiveness of our optimal control scheme is shown through a comprehensiverobotic case study comparing against a heuristic control method for deployingrods for a wide variety of patterns. In addition to this, we also showcase thepracticality of our control scheme by having a robot accomplish challenginghigh-level tasks such as mimicking human handwriting, cable placement, andtying knots.

Sim2Real Neural Controllers for Physics-based Robotic Deployment of Deformable Linear Objects