为可变形物体操作设计实时安全过滤器，确保任务安全且无需重训练。
Online Safety Filter for Deformable Object Manipulation with Horizon Agnostic Neural Operators
- 用无时域依赖的神经算子建模物理动态，支持不同时间长度的泛化。
- 通过轻量级二次规划实现任务级安全约束，安全轨迹率提升22%。
- 适合高风险机器人操作场景，如流体、布料等柔性材料控制。
涉及流体、布料和软体等可变形介质的机器人操控任务的安全控制仍具挑战性，因现有基于学习的方法通过奖励塑造间接编码安全，无法保证部署时约束满足。本文提出一种基于约束的在线安全过滤器，通过最小修改任意基础控制策略，在实时中显式执行任务级安全约束。方法结合两个核心组件：一种无时域依赖的神经算子，学习底层偏微分方程（PDE）动力学的边界输入输出映射，无需重训练即可跨不同滚动长度泛化；以及一种边界控制屏障函数，通过轻量级二次规划在任务相关输出层面认证安全。所得安全约束关于边界输入速率呈仿射形式，支持实时在线过滤。在FluidLab的流体操控任务上评估，该方法相比未过滤的基础策略，安全轨迹率最高提升22%，同时减少到达安全集所需的步数，证明了基于约束的安全保障比奖励塑造更可靠且高效。
原文摘要 · Abstract (English)
Safety critical control of robotic manipulation tasks involving deformable media such as fluids, cloth, and soft objects remains challenging because existing learning based approaches encode safety indirectly through reward shaping, which provides no guarantee of constraint satisfaction at deployment. We present a constraint driven online safety filter for deformable object manipulation that enforces explicit task level safety constraints in real time by minimally modifying any nominal control policy. Our approach combines two key components: a horizon agnostic neural operator that learns the boundary input output mapping of the underlying PDE dynamics and generalizes across variable rollout lengths without retraining, and a boundary control barrier function that certifies safety at the task relevant output level via a lightweight quadratic program. The resulting safety constraint is affine in the boundary input rate, enabling real time online filtering. We evaluate the proposed method on fluid manipulation tasks in FluidLab, where the filter improves safe trajectory rates by up to 22% over unfiltered base policies while also reducing the number of steps required to reach the safe set, demonstrating that constraint driven safety enforcement is both more reliable and more efficient than reward shaping approaches.
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