论文标题
自适应安全运动计划的不确定性感知的约束学习
Uncertainty-Aware Constraint Learning for Adaptive Safe Motion Planning from Demonstrations
论文作者
论文摘要
我们提出了一种学习来满足示范中不确定约束的方法。我们的方法使用强大的优化来获得对与示威相一致的潜在无限可能约束的信念，然后利用这种信念来计划以满足可能的约束来折衷绩效的轨迹。我们在闭环策略中使用这些轨迹，该策略使用信念更新执行和补充，这些更新结合了执行过程中收集的数据。我们得出了限制信念的准确性和计划安全性的概率保证的保证。我们在7-DOF ARM和12D四型方面介绍了结果，这表明我们的方法可以学会满足高维（最高30d）的不确定限制，并且在安全性和效率方面优于基准。
We present a method for learning to satisfy uncertain constraints from demonstrations. Our method uses robust optimization to obtain a belief over the potentially infinite set of possible constraints consistent with the demonstrations, and then uses this belief to plan trajectories that trade off performance with satisfying the possible constraints. We use these trajectories in a closed-loop policy that executes and replans using belief updates, which incorporate data gathered during execution. We derive guarantees on the accuracy of our constraint belief and probabilistic guarantees on plan safety. We present results on a 7-DOF arm and 12D quadrotor, showing our method can learn to satisfy high-dimensional (up to 30D) uncertain constraints, and outperforms baselines in safety and efficiency.
