We present Learned Risk Metric Maps (LRMM) for real-time estimation of coherent risk metrics of high dimensional dynamical systems operating in unstructured, partially observed environments. LRMM models are simple to design and train -- requiring only procedural generation of obstacle sets, state and control sampling, and supervised training of a function approximator -- which makes them broadly applicable to arbitrary system dynamics and obstacle sets. In a parallel autonomy setting, we demonstrate the model's ability to rapidly infer collision probabilities of a fast-moving car-like robot driving recklessly in an obstructed environment; allowing the LRMM agent to intervene, take control of the vehicle, and avoid collisions. In this time-critical scenario, we show that LRMMs can evaluate risk metrics 20-100x times faster than alternative safety algorithms based on control barrier functions (CBFs) and Hamilton-Jacobi reachability (HJ-reach), leading to 5-15\% fewer obstacle collisions by the LRMM agent than CBFs and HJ-reach. This performance improvement comes in spite of the fact that the LRMM model only has access to local/partial observation of obstacles, whereas the CBF and HJ-reach agents are granted privileged/global information. We also show that our model can be equally well trained on a 12-dimensional quadrotor system operating in an obstructed indoor environment. The LRMM codebase is provided at https://github.com/mit-drl/pyrmm.

翻译：我们提出学习风险度量映射（LRMM），用于实时估计在非结构化、部分观测环境中运行的高维动力系统的一致性风险度量。LRMM模型设计简单且易于训练——仅需程序化生成障碍物集合、状态与控制采样，以及函数逼近器的监督训练——这使得其可广泛适用于任意系统动力学与障碍物集合。在并行自主场景中，我们展示了该模型快速推断在障碍环境中鲁莽行驶的类车机器人碰撞概率的能力，使LRMM代理能够干预、接管车辆控制并避免碰撞。在此类时间关键场景中，我们证明LRMM评估风险度量的速度比基于控制屏障函数（CBF）和汉密尔顿-雅可比可达性（HJ-reach）的安全算法快20-100倍，导致LRMM代理的障碍碰撞次数比CBF和HJ-reach减少5-15%。尽管LRMM模型仅能获取障碍物的局部/部分观测，而CBF与HJ-reach代理拥有特权/全局信息，这一性能提升依然成立。我们还展示了该模型可同样良好地训练用于在室内障碍环境中运行的12维四旋翼系统。LRMM代码库已发布在https://github.com/mit-drl/pyrmm。