SILO: Simulation-in-the-Loop Sim-to-Real Transfer for Multi-Stage Cable Routing
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 enabl
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 52%AgileRL/AgileRL →
- PossiblePossibly related (embedding) · 46%RL without TD learning →
- PossiblePossibly related (embedding) · 46%Gradient-based Planning for World Models at Longer Horizons →
- LinkedLinked via arxiv author · 85%Stone Tao →
“SILO: Simulation-in-the-Loop Sim-to-Real Transfer for Multi-Stage Cable Routing”
- LinkedLinked via arxiv author · 85%Jie Xu →
“SILO: Simulation-in-the-Loop Sim-to-Real Transfer for Multi-Stage Cable Routing”
- LinkedLinked via arxiv author · 85%Hesam Rabeti →
“SILO: Simulation-in-the-Loop Sim-to-Real Transfer for Multi-Stage Cable Routing”
- LinkedLinked via arxiv author · 85%Yashraj Narang →
“SILO: Simulation-in-the-Loop Sim-to-Real Transfer for Multi-Stage Cable Routing”
- LinkedLinked via arxiv author · 85%Yijie Guo →
“SILO: Simulation-in-the-Loop Sim-to-Real Transfer for Multi-Stage Cable Routing”