论文标题
从最佳控制中学习敏捷路径
Learning Agile Paths from Optimal Control
论文作者
论文摘要
有效的运动计划算法对于在现实世界中部署机器人至关重要。不幸的是，为了可行性，这些算法通常会大大降低问题的维度，从而提出最佳解决方案。该限制最容易在敏捷机器人中观察到，在敏捷机器人中，解决方案空间可以具有多个额外的维度。最佳控制方法通过在不牺牲环境的复杂性的情况下找到最佳解决方案来部分解决此问题，但不能满足现实世界应用的效率需求。这项工作提出了一种方法，通过训练机器学习模型的最佳控制方法的输出来同时解决这些问题。
Efficient motion planning algorithms are of central importance for deploying robots in the real world. Unfortunately, these algorithms often drastically reduce the dimensionality of the problem for the sake of feasibility, thereby foregoing optimal solutions. This limitation is most readily observed in agile robots, where the solution space can have multiple additional dimensions. Optimal control approaches partially solve this problem by finding optimal solutions without sacrificing the complexity of the environment, but do not meet the efficiency demands of real-world applications. This work proposes an approach to resolve these issues simultaneously by training a machine learning model on the outputs of an optimal control approach.
