用神经网络保证黑箱机器人安全穿越狭窄障碍
Guaranteed Reach-Avoid for Black-Box Systems through Narrow Gaps via Neural Network Reachability
- 基于ReLU神经网络建模黑箱系统，结合误差分析计算可达集
- 在仿真与实车中实现安全极限泊车，应对大扰动仍可靠
- 适合自动驾驶、强化学习控制等不可知动态系统
经典避障-到达问题中，自主移动机器人需抵达目标同时避开障碍。当障碍形成狭窄通道且机器人为黑箱（动力学未知但可交互）时，难以提供性能保证。本文提出NeuralPARC方法，将先前分段仿射可达性计算（PARC）扩展至由修正线性单元（ReLU）神经网络建模的系统，该网络通过机器人演示的参数化轨迹数据训练而成。NeuralPARC在考虑建模误差的前提下，计算神经网络的可达集，输出一组状态与参数，确保黑箱系统可安全抵达目标并避开障碍。实验表明，NeuralPARC优于PARC，在仿真与真实模型车上实现了安全的极限车辆漂移泊车，且使受强扰动的自主水面艇（ASV）在深度强化学习策略控制下依然保持安全。
原文摘要 · Abstract (English)
In the classical reach-avoid problem, autonomous mobile robots are tasked to reach a goal while avoiding obstacles. However, it is difficult to provide guarantees on the robot's performance when the obstacles form a narrow gap and the robot is a black-box (i.e. the dynamics are not known analytically, but interacting with the system is cheap). To address this challenge, this paper presents NeuralPARC. The method extends the authors' prior Piecewise Affine Reach-avoid Computation (PARC) method to systems modeled by rectified linear unit (ReLU) neural networks, which are trained to represent parameterized trajectory data demonstrated by the robot. NeuralPARC computes the reachable set of the network while accounting for modeling error, and returns a set of states and parameters with which the black-box system is guaranteed to reach the goal and avoid obstacles. NeuralPARC is shown to outperform PARC, generating provably-safe extreme vehicle drift parking maneuvers in simulations and in real life on a model car, as well as enabling safety on an autonomous surface vehicle (ASV) subjected to large disturbances and controlled by a deep reinforcement learning (RL) policy.
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