We present the Learning for KinoDynamic Tree Expansion (L4KDE) method for kinodynamic planning. Tree-based planning approaches, such as rapidly exploring random tree (RRT), are the dominant approach to finding globally optimal plans in continuous state-space motion planning. Central to these approaches is tree-expansion, the procedure in which new nodes are added into an ever-expanding tree. We study the kinodynamic variants of tree-based planning, where we have known system dynamics and kinematic constraints. In the interest of quickly selecting nodes to connect newly sampled coordinates, existing methods typically cannot optimise to find nodes that have low cost to transition to sampled coordinates. Instead, they use metrics like Euclidean distance between coordinates as a heuristic for selecting candidate nodes to connect to the search tree. We propose L4KDE to address this issue. L4KDE uses a neural network to predict transition costs between queried states, which can be efficiently computed in batch, providing much higher quality estimates of transition cost compared to commonly used heuristics while maintaining almost-surely asymptotic optimality guarantee. We empirically demonstrate the significant performance improvement provided by L4KDE on a variety of challenging system dynamics, with the ability to generalise across different instances of the same model class, and in conjunction with a suite of modern tree-based motion planners.

翻译：我们提出了一种面向运动动力学子树扩展的学习方法（L4KDE），用于解决运动动力学规划问题。基于树的规划方法（如快速扩展随机树，RRT）是在连续状态空间运动规划中寻找全局最优方案的主流方法。树扩展作为这类方法的核心步骤，通过不断向树中添加新节点实现扩张。本研究针对基于树的规划方法中的运动动力学变体展开，其中系统动力学特性和运动学约束均为已知。为快速选择与新采样坐标连接的节点，现有方法通常无法优化并找到实现该过渡成本最低的节点。相反，它们采用诸如坐标间欧氏距离等度量作为启发式方法，以选择候选节点连接至搜索树。我们提出的L4KDE方法旨在解决该问题。该方法利用神经网络预测状态间的过渡成本，并支持批量高效计算。与常用启发式方法相比，L4KDE能够提供质量更高的过渡成本估计，同时保持几乎必然的渐近最优性保证。通过在多种具有挑战性的系统动力学场景下的实证研究，我们验证了L4KDE显著的性能提升——它不仅能在同一模型类别的不同实例间实现泛化，还可与一系列现代基于树的运动规划器协同工作。