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Robotic manipulation of deformable linear objects (DLOs) has great potentialfor applications in diverse fields such as agriculture or industry. However, amajor challenge lies in acquiring accurate deformation models that describe therelationship between robot motion and DLO deformations. Such models aredifficult to calculate analytically and vary among DLOs. Consequently,manipulating DLOs poses significant challenges, particularly in achieving largedeformations that require highly accurate global models. To address thesechallenges, this paper presents MultiAC6: a new multi Actor-Critic frameworkfor robot action space decomposition to control large 3D deformations of DLOs.In our approach, two deep reinforcement learning (DRL) agents orient andposition a robot gripper to deform a DLO into the desired shape. Unlikeprevious DRL-based studies, MultiAC6 is able to solve the sim-to-real gap,achieving large 3D deformations up to 40 cm in real-world settings.Experimental results also show that MultiAC6 has a 66\% higher success ratethan a single-agent approach. Further experimental studies demonstrate thatMultiAC6 generalizes well, without retraining, to DLOs with different lengthsor materials.

Multi Actor-Critic DDPG for Robot Action Space Decomposition: A Framework to Control Large 3D Deformation of Soft Linear Objects