Quickly and reliably finding accurate inverse kinematics (IK) solutions remains a challenging problem for robotic manipulation. Existing numerical solvers are broadly applicable, but typically only produce a single solution and rely on local search techniques to minimize highly nonconvex objective functions. More recent learning-based approaches that approximate the entire feasible set of solutions have shown promise as a means to generate multiple fast and accurate IK results in parallel. However, existing learning-based techniques have a significant drawback: each robot of interest requires a specialized model that must be trained from scratch. To address this key shortcoming, we investigate a novel distance-geometric robot representation coupled with a graph structure that allows us to leverage the flexibility of graph neural networks (GNNs). We use this approach to train the first learned generative graphical inverse kinematics (GGIK) solver that is able to produce a large number of diverse solutions in parallel and to also generalize: a single learned model can be used to produce IK solutions for a variety of different robots. When compared to several other learned IK methods, GGIK provides more accurate solutions. GGIK is also able to generalize reasonably well to robot manipulators unseen during training. Finally, we show that GGIK can be used to complement local IK solvers by providing reliable initializations to seed the local optimization process.

翻译：快速可靠地找到精确的逆运动学（IK）解仍然是机器人操作中的一个具有挑战性的问题。现有的数值求解器适用范围广泛，但通常只能产生单一解，并且依赖局部搜索技术来最小化高度非凸的目标函数。最近基于学习的方法近似整个可行解集，已展现出并行生成多个快速且精确IK解的潜力。然而，现有基于学习的技术存在一个显著缺陷：每个目标机器人需要从零开始训练专门模型。为解决这一关键不足，我们研究了一种新颖的距离几何机器人表示方法，并结合图形结构以利用图神经网络（GNN）的灵活性。利用该方法，我们训练了首个生成式图形逆运动学（GGIK）求解器，它能并行生成大量多样化解，并具备泛化能力：单个学习模型可为多种不同机器人生成IK解。与多种其他基于学习的IK方法相比，GGIK能提供更精确的解。GGIK还能合理泛化至训练中未见过的机器人操作臂。最后，我们证明GGIK可为局部IK求解器提供可靠初始化，从而辅助局部优化过程。