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English(EN) SILO: Simulation-in-the-Loop Sim-to-Real Transfer for Multi-Stage Cable Routing

新的SILO框架增强了机器人布线任务的仿真到现实迁移能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的模拟回路中的仿真 (SILO) 强化学习框架，用于多阶段布线任务。该方法利用GPU并行仿真来近似线性可变形行为，使策略能够泛化到各种电缆几何形状和变形模式。SILO框架结合了局部强化学习策略和鲁棒的电缆状态估计，以弥合仿真到现实的差距，在真实世界任务中与先前最先进的方法相比，成功率更高，周期时间缩短了一半。 AI

影响 这项研究可以显著提高涉及电缆等可变形物体的机器人操作任务的效率和成功率。

排序理由 该集群包含一篇详细介绍新研究框架和方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的SILO框架增强了机器人布线任务的仿真到现实迁移能力

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该集群包含一篇详细介绍新研究框架和方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Stone Tao, Jie Xu, Hesam Rabeti, Yashraj Narang, Yijie Guo, Iretiayo Akinola ·

    SILO: 仿真回路仿真到真实迁移用于多阶段布线

    arXiv:2607.04616v1 Announce Type: cross Abstract: 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 set…

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