用于生成式图逆运动学的欧几里得等变模型
Euclidean Equivariant Models for Generative Graphical Inverse Kinematics
AI总结：
针对现有学习方法需为每个机器人单独训练模型的问题，本文提出基于距离几何图表示和图神经网络的生成式图逆运动学求解器GGIK，利用欧几里得等变性实现单一模型为多种机器人并行生成多样IK解。
摘要：
快速且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挑战性问题。现有的数值求解器通常只产生单一解，并依赖局部搜索技术来最小化高度非凸的目标函数。近年来，基于学习的方法通过近似整个可行解集，展现出并行生成多个快速且精确的IK结果的前景。然而，现有基于学习的技术存在一个显著缺点：每个感兴趣的机器人都需要一个必须从头开始训练的专用模型。为了解决这一不足，我们研究了一种新颖的距离几何机器人表示，并结合图结构，使我们能够利用图神经网络（GNN）的灵活性。我们使用这种方法训练了一个生成式图逆运动学求解器（GGIK），它能够并行产生大量多样的解，同时具有良好的泛化能力——单个学习模型可用于为多种不同机器人产生IK解。该图公式优雅地揭示了IK问题中源于机器人操作臂空间性质的对称性和欧几里得等变性。我们通过将这种对称性编码到学习模型的架构中来利用它，从而得到一个灵活的求解器，能够为多个机器人生成IK解集。
英文摘要：
Quickly and reliably finding accurate inverse kinematics (IK) solutions remains a challenging problem for robotic manipulation. Existing numerical solvers typically produce a single solution only and rely on local search techniques to minimize a highly nonconvex objective function. Recently, 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 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 a generative graphical inverse kinematics solver (GGIK) that is able to produce a large number of diverse solutions in parallel while also generalizing well -- a single learned model can be used to produce IK solutions for a variety of different robots. The graphical formulation elegantly exposes the symmetry and Euclidean equivariance of the IK problem that stems from the spatial nature of robot manipulators. We exploit this symmetry by encoding it into the architecture of our learned model, yielding a flexible solver that is able to produce sets of IK solutions for multiple robots.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
