Adaptive Curvature-Aware Routing for Stiff Cable Control Via Dual Manipulation

Deformable Linear Objects (DLOs), such as cables and ropes, pose significant challenges for robotic manipulation due to their high-dimensional state space, nonlinear defor- mation dynamics, and strong sensitivity to external forces. Cable routing tasks, in particular, are further complicated by geometric constraints, residual stresses in stiff cables, and the necessity of precise alignment with designated connectors. Existing approaches often rely on endpoint manipulation or external fixtures, which limits flexibility and scalability in real-world applications. While data-driven and graph-based models have shown promise for flexible ropes, they struggle to generalize across varying cable stiffness and suffers high computational costs. To address these challenges, we propose Adaptive Curvature-Aware Routing (ACR), a dual manipu- lation framework capable of adaptively handling cables of high stiffness and arbitrary lengths. Specifically, our frame- work combines local curvature analysis with Radial Basis Function Networks (RBFNs) to predict cable deformations. By prioritizing regions with high curvature discrepancies, it adaptively selects manipulation segments and performs safe, precise corrective actions to shape the cable toward the target configuration without heavy reliance on fixtures. Furthermore, we develop a constraint-aware cooperative controller that integrates both kinematic feasibility and physical safety into the motion strategy. Experiments in both simulation and real- world setups demonstrate that ACR significantly outperforms baseline methods in terms of success rate and terminal accu- racy, validating the effectiveness of combining curvature-based adaptivity with data-driven modeling for complex cable routing tasks.