SILO: Simulation-in-the-Loop Sim-to-Real Transfer for Multi-Stage Cable Routing
For robotic manipulation of linear deformable objects, this work provides the first successful sim-to-real transfer of RL policies for multi-stage cable routing, addressing a key bottleneck in scalability and generalization.
This paper introduces a sim-to-real reinforcement learning framework for multi-stage cable routing that uses GPU-parallelized simulation to train policies generalizing across diverse cable geometries. On real-world tasks, it achieves higher success rates and 2x reduction in cycle times compared to prior state-of-the-art learning methods.
Linear-deformable manipulation remains challenging due to the complex deformations of objects such as cables and ropes. Prior data-driven approaches, particularly imitation learning, have shown some promise in narrowly defined settings but typically require thousands of demonstrations for specific tasks and cable types, limiting scalability and generalization. We introduce a sim-to-real reinforcement learning (RL) framework for multi-stage cable routing that leverages GPU-parallelized simulation to approximate linear deformable behaviors. Training across thousands of parallel simulations enables the learned policies to generalize across diverse cable geometries and deformation patterns. To bridge the sim-to-real gap, we propose a novel deployment strategy that combines a Simulation In the LOop (SILO) execution framework, localized RL policies, and robust cable state estimation. On real-world cable routing tasks, our approach achieves higher success rates and 2x reduction in cycle times compared to prior state-of-the-art learning methods. To our knowledge, this is the first successful sim-to-real transfer of RL policies for multi-stage cable routing. Videos and additional visualizations are available at https://silo-cable-routing.github.io/