无安全神谕下通过模型预测控制近似安全反馈
Approximating Safety Feedback Without a Safety Oracle via Model Predictive Control
AI总结：
提出一种利用模拟器和模型预测路径积分算法，基于可逆性和正不变性假设来近似安全函数的方法，避免手动设计安全反馈。
摘要：
移动机器人控制的安全决策算法通常需要存在反馈来验证提议动作的安全性。该反馈假定在控制系统的开发或部署过程中直接可用，可以采取显式约束公式或手工标记的安全数据集的形式，但两者都可能不准确或耗时。许多最近开发的模拟器可以处理复杂的交互和多样化的环境。这些环境具有隐式安全约束，可能难以建模。通过利用其中一个模拟器，我们可以构建一个安全函数的代理，从而绕过对手动设计反馈来捕获这些约束的需求。我们提出了一种算法，通过使用可逆性和对不安全状态空间的正不变性假设来近似安全性。该方法采用模型预测路径积分算法（MPPI）来建立这种可逆性并验证提议的动作。首先，通过模拟器将动作投影到未来状态。然后，如果MPPI能够找到一条路径返回到轨迹中的先前状态，则该状态保证在不安全（正不变）集合之外。实验结果表明，所提出的算法可以近似安全神谕的性能，同时避免将不安全状态分类为安全。
英文摘要：
Safe decision-making algorithms for control of mobile robots often require the existence of feedback to verify the safety of proposed actions. This feedback is assumed to be directly available during the development or deployment of the control system. It can take the form of either an explicit constraint formulation or a set of hand-labeled safety data, both of which can be inaccurate or time consuming to produce. Many recently developed simulators can handle complex interactions and varied environments. These environments have implicit safety constraints that may be hard to model. By leveraging one of these simulators, we can construct a proxy for a safety function that bypasses the need for hand designed feedback in capturing these constraints. We present an algorithm that approximates safety by using reversibility and a positive-invariance assumption on the unsafe state space. This method employs the Model-Predictive Path Integral algorithm (MPPI) to establish this reversibility and verify a proposed action. First the action is projected via the simulator to a future state. Then if MPPI can find a path back to a previous state in the trajectory, that state is guaranteed to be outside the unsafe (positive invariant) set. Experimental results demonstrate that the proposed algorithm can approximate the performance of a safety oracle while avoiding classification of unsafe states as safe.
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。
