AnySafe：通过潜空间中的安全约束参数化在运行时调整潜在安全过滤器
AnySafe: Adapting Latent Safety Filters at Runtime via Safety Constraint Parameterization in the Latent Space
AI总结：
本文提出约束参数化潜在安全过滤器，通过潜空间相似性和保形校准，使安全过滤器在运行时适应用户指定的任意安全约束，并在Franka机械臂的仿真和硬件实验中验证了有效性。
摘要：
近期研究表明，基础安全控制方法（如Hamilton-Jacobi（HJ）可达性分析）可应用于世界模型的潜空间。尽管这为难以建模的基于视觉的任务合成了潜在安全过滤器，但这些方法假设安全约束先验已知且在部署期间保持不变，限制了安全过滤器在不同场景中的适应性。为解决此问题，我们提出了约束参数化的潜在安全过滤器，可在运行时适应用户指定的安全约束。我们的关键思想是通过潜空间相似性度量，以表示约束的图像的编码为条件来定义安全约束。通过与失败相似性的概念通过保形校准以原则性方式对齐，该校准控制系统接近约束表示的程度。参数化安全过滤器完全在世界模型的想象中训练，将模型看到的任何图像视为潜在的测试时约束，从而实现对任意安全约束的运行时适应。在基于视觉的控制任务（使用Franka机械臂）的仿真和硬件实验中，我们展示了我们的方法通过以用户指定约束图像的编码为条件在运行时适应，且不牺牲性能。视频结果可在https://any-safe.github.io找到。
英文摘要：
Recent works have shown that foundational safe control methods, such as Hamilton-Jacobi (HJ) reachability analysis, can be applied in the latent space of world models. While this enables the synthesis of latent safety filters for hard-to-model vision-based tasks, they assume that the safety constraint is known a priori and remains fixed during deployment, limiting the safety filter's adaptability across scenarios. To address this, we propose constraint-parameterized latent safety filters that can adapt to user-specified safety constraints at runtime. Our key idea is to define safety constraints by conditioning on an encoding of an image that represents a constraint, using a latent-space similarity measure. The notion of similarity to failure is aligned in a principled way through conformal calibration, which controls how closely the system may approach the constraint representation. The parameterized safety filter is trained entirely within the world model's imagination, treating any image seen by the model as a potential test-time constraint, thereby enabling runtime adaptation to arbitrary safety constraints. In simulation and hardware experiments on vision-based control tasks with a Franka manipulator, we show that our method adapts at runtime by conditioning on the encoding of user-specified constraint images, without sacrificing performance. Video results can be found on https://any-safe.github.io
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
