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 " 实战风险计量图 " (LIMM),用于实时估计在非结构化、部分观测环境中运行的高维动态系统的一致性风险度量。LIMM模型在设计和培训方面比较简单 -- -- 只需要在程序上产生障碍、进行状态和监控抽样,并监督对功能匹配器的培训 -- -- 这使得这些模型广泛适用于任意的系统动态和障碍组。在平行的自主环境下,我们展示了该模型能够迅速推断快速移动的汽车式机器人在障碍环境中鲁莽驾驶的快速移动式机器人的碰撞概率;允许LIMM代理进行干预、控制车辆和避免碰撞。在这种时间危急的情况下,我们显示LMMMMM模型可以比基于控制屏障功能(CBFs)和汉密尔顿-贾科比(HJ-Accer)的替代安全算法(HJ)更快地评估20-100倍的风险度标准值。比基于控制屏障功能(CBFF)和汉密尔顿-贾科比(HJ-RM)的可达性碰撞模型比比(CBFBFM/HJ-C)的模型减少了5-15障碍。尽管LMMMMM/HLMM/C的模型提供了对当地标准的操作标准,但我们的操作,但的操作系统也同样展示了标准。</s>