用仿真闭环框架实现复杂电缆多阶段布线的高效真实迁移
SILO: Simulation-in-the-Loop Sim-to-Real Transfer for Multi-Stage Cable Routing
- 通过并行仿真训练强化学习策略，支持多种电缆形态和变形模式
- 真实场景中成功率更高，周期时间缩短至原有方法的1/2
- 适合机器人抓取、自动化装配等需要柔性物体操作的领域
线性可变形物体的操作（如电缆、绳索）因复杂形变而极具挑战。以往基于数据的方法，尤其是模仿学习，在特定任务和电缆类型上表现有限，通常需数千次演示才能训练，难以扩展和泛化。本文提出一种面向多阶段电缆布线的仿真-真实强化学习框架，利用GPU并行仿真近似线性可变形行为。在数千个并行仿真中训练使策略能跨不同电缆几何形状与变形模式泛化。为弥合仿真与真实间的差距，我们引入创新部署策略：仿真闭环（SILO）执行框架、局部强化学习策略与鲁棒电缆状态估计。在真实电缆布线任务中，该方法成功率达更高，且周期时间减少2倍，相比现有最先进学习方法。据我们所知，这是首个成功实现多阶段电缆布线强化学习策略从仿真到真实的迁移。视频与可视化见 https://silo-cable-routing.github.io/
原文摘要 · Abstract (English)
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/
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。