English(EN) CALOS: Control-Affine Lyapunov On-manifold Safety Layer for Safe Deep Reinforcement Learning for Quadrotors

四旋翼无人机深度强化学习的新安全层

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-17 04:00

研究人员开发了CALOS（流形上控制仿射李雅普诺夫安全层），旨在强制执行四旋翼无人机深度强化学习中的安全约束。该运行时层将姿态和李雅普诺夫下降条件构建为二次规划问题，从而能够实时校正策略的扭矩输出。在NVIDIA Isaac Lab中进行测试，CALOS显著减少了横向跟踪误差，消除了姿态约束违规，同时还加速了训练收敛并提高了数据效率。 AI

影响增强了机器人控制应用中AI训练的安全性和效率。

排序理由详细介绍一种新的安全深度强化学习方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Fabrizio Cesareo, Sebastiano Mengozzi, Nicola Mimmo, Andrea Acquaviva · 2026-09-17 04:00

CALOS：用于四旋翼安全深度强化学习的控制仿射李雅普诺夫流形上安全层

arXiv:2609.17758v1 Announce Type: cross Abstract: Deep Reinforcement Learning has demonstrated remarkable capability in quadrotor control, yet learned policies offer no guarantee of respecting safety constraints during training or deployment. We present CALOS (Control-Affine Lyap…

本文如何被排名

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CALOS：用于四旋翼安全深度强化学习的控制仿射李雅普诺夫流形上安全层

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