Robotic manipulation of deformable linear objects (DLOs) has great potential for applications in diverse fields such as agriculture or industry. However, a major challenge lies in acquiring accurate deformation models that describe the relationship between robot motion and DLO deformations. Such models are difficult to calculate analytically and vary among DLOs. Consequently, manipulating DLOs poses significant challenges, particularly in achieving large deformations that require highly accurate global models. To address these challenges, this paper presents MultiAC6: a new multi Actor-Critic framework for robot action space decomposition to control large 3D deformations of DLOs. In our approach, two deep reinforcement learning (DRL) agents orient and position a robot gripper to deform a DLO into the desired shape. Unlike previous 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 rate than a single-agent approach. Further experimental studies demonstrate that MultiAC6 generalizes well, without retraining, to DLOs with different lengths or materials.

翻译：软体线性物体（DLO）的机器人操作在农业、工业等领域具有广阔应用前景，但核心挑战在于精确获取描述机器人运动与DLO变形关系的变形模型。此类模型难以通过解析计算获得，且因物体材质不同而变化。因此，DLO操作面临重大难题，尤其在需要高精度全局模型的大尺度变形场景中。为解决上述挑战，本文提出MultiAC6：一种新型多智能体Actor-Critic框架，通过机器人动作空间分解实现DLO大尺度三维变形控制。该方法中，两个深度强化学习（DRL）智能体分别控制机器人夹爪的方位与位置，将DLO变形至目标形状。不同于先前基于DRL的研究，MultiAC6成功跨越仿真到现实（sim-to-real）鸿沟，在真实环境中实现了达40厘米的大尺度三维变形。实验结果表明，MultiAC6的成功率较单智能体方法提升66%。进一步实验证实，该框架无需重新训练即可泛化至不同长度或材质的DLO。