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LyEvO框架增强安全仿真到现实策略迁移 · 跟踪2个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出LyEvO，一个旨在增强从仿真到现实应用策略的安全性和鲁棒性的新框架。该方法集成了约束进化优化、统计模型检查和基于Lyapunov的稳定性分析。通过利用系统动力学知识，LyEvO计算初始稳定性区域，并通过联合优化和验证迭代改进它，提供部署就绪标准。在Cartpole和3D Quadrotor基准测试（包括真实世界实验）上的评估表明，成功实现了安全且鲁棒的仿真到现实迁移。 AI

影响 提高了机器人实际应用中AI控制器的可靠性。

排序理由 该集群包含一篇详细介绍机器人策略学习新方法的学术论文。

在 arXiv cs.NE (Neural & Evolutionary) 阅读 →

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LyEvO框架增强安全仿真到现实策略迁移 · 跟踪2个来源

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该集群包含一篇详细介绍机器人策略学习新方法的学术论文。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Riccardo Curcio, Hongpeng Cao, Marco Caccamo ·

    LyEvO：用于安全鲁棒的Sim-to-Real策略学习的Lyapunov引导进化优化

    arXiv:2608.06481v1 Announce Type: cross Abstract: Training controllers that are safe and robust in simulation, and systematically assessing their readiness for real-world deployment, remain key challenges in sim-to-real transfer. To address this, we propose LyEvO, a physics-groun…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Marco Caccamo ·

    LyEvO：用于安全鲁棒的Sim-to-Real策略学习的Lyapunov引导进化优化

    Training controllers that are safe and robust in simulation, and systematically assessing their readiness for real-world deployment, remain key challenges in sim-to-real transfer. To address this, we propose LyEvO, a physics-grounded framework that combines constrained Evolutiona…

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