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This paper introduces a motion planning algorithm specifically designed for arranging deformable linear objects (DLOs) into target configurations, addressing the challenges posed by large action spaces and complex dynamics. By combining sampling techniques with gradient-based optimization, the algorithm effectively identifies robust sequences of grasps and control inputs that minimize sensitivity to model noise. Experimental results in both simulation and real-world settings validate the algorithm's effectiveness in achieving precise manipulation of DLOs.
Achieving robust manipulation of deformable linear objects could revolutionize automation in household tasks and manufacturing.
Robotic manipulation is a fundamental challenge in the pursuit of automating household tasks and advancing robotic manufacturing. Manipulation planning for deformable objects is particularly challenging due to the associated large action spaces and complex dynamics. For deformable objects, some actions are sensitive to noise in the model, which can significantly degrade the accuracy of predicted trajectories. In this letter, we present a motion planning algorithm for arranging deformable linear objects (DLOs) into user-provided goal configurations. Using a novel problem formulation as well as a combination of sampling and gradient based optimization, the algorithm finds sequences of grasps and control inputs that are robust. We demonstrate the effectiveness of the new algorithm using numerical experiments and manipulation examples both in simulation and in real world environments.
ETH