用深度算子学习实现安全控制器跨环境自适应。
Domain Adaptive Safety Filters via Deep Operator Learning
- 通过参数化偏微分方程残差学习环境参数到安全屏障函数的映射。
- 在动态障碍物导航任务中验证，无需重训练即可适配新环境。
- 适合需要快速迁移的安全控制场景，如自动驾驶、机器人避障。
基于学习的方法正被广泛探索用于构建安全关键控制系统的控制屏障函数（CBFs）。然而，这些方法在应用于未见环境时通常需要完全重新训练，限制了其适应性。为此，我们提出一种自监督的深度算子学习框架，不直接学习CBF，而是学习从环境参数到对应CBF的映射。该方法利用参数化偏微分方程（PDE）的残差，其解定义了一个参数化CBF，近似最大可控不变集。该框架可处理复杂安全约束、高相对阶系统及执行器限制。我们在涉及动态障碍物的导航任务中通过数值实验验证了方法的有效性。
原文摘要 · Abstract (English)
Learning-based approaches for constructing Control Barrier Functions (CBFs) are increasingly being explored for safety-critical control systems. However, these methods typically require complete retraining when applied to unseen environments, limiting their adaptability. To address this, we propose a self-supervised deep operator learning framework that learns the mapping from environmental parameters to the corresponding CBF, rather than learning the CBF directly. Our approach leverages the residual of a parametric Partial Differential Equation (PDE), where the solution defines a parametric CBF approximating the maximal control invariant set. This framework accommodates complex safety constraints, higher relative degrees, and actuation limits. We demonstrate the effectiveness of the method through numerical experiments on navigation tasks involving dynamic obstacles.
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