LEAP-CBF：具有最小努力对抗势的不确定系统安全过滤器
LEAP-CBF: A Safety Filter for Uncertain Systems with Least-Effort Adversarial Potentials
- Massachusetts Institute of Technology（麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出LEAP证书，量化扰动导致失败所需努力，并基于其构造鲁棒安全过滤器，通过深度强化学习构建，仿真和硬件实验验证其有效性。
AI中文摘要：
控制障碍函数（CBF）是一种流行的安全过滤器，用于确保非线性动力系统的安全性。然而，当系统受到不确定性和扰动影响时，这需要使用CBF的鲁棒变体，而这些变体可能难以构造且过于保守，尤其是在输入约束下的高维系统中。在本工作中，我们提出了一种新方法来解决这些挑战，通过引入最小努力对抗势（LEAP），这是一种证书，用于根据扰动导致失败所需的努力来量化给定状态对扰动的鲁棒性。我们证明了LEAP对于无扰动系统是一个CBF，但也可以用于构造一个对累积努力有界的扰动具有鲁棒性的安全过滤器。我们提出了一种使用在线策略深度强化学习来构造LEAP的方法。接下来，我们在具有扰动和不确定性的多种多智能体系统的仿真中展示了LEAP。最后，在四足机器人和四旋翼飞行器上的硬件实验验证了LEAP非常适合应对真实世界机器人系统中的扰动和不确定性。
英文摘要：
Control barrier functions (CBF) are a popular safety filter to ensure safety for nonlinear dynamical systems. However, when the system is subject to uncertainties and disturbances, this requires the use of robust variants of CBFs, which can be difficult to construct and can be overly conservative, especially for high-dimensional systems under input constraints. In this work, we propose a new approach to solve these challenges by introducing Least-Effort Adversarial Potentials (LEAP), a certificate that quantifies the robustness of a given state against disturbances in terms of the effort required by the disturbance to cause failure. We show that LEAP is a CBF for the undisturbed system, but can also be used to construct a safety filter that is robust to disturbances whose cumulative effort is bounded. We propose a method for constructing LEAPs with on-policy deep reinforcement learning. Next, we demonstrate LEAPs in simulation on a variety of multi-agent systems with disturbances and uncertainties. Finally, hardware experiments on a quadruped and quadrotors validate that LEAPs are well suited to tackle the disturbances and uncertainties from real-world robotic systems.
