论文标题
神经逆运动学
Neural Inverse Kinematics
论文作者
论文摘要
鉴于运动链中选定元素的所需位置，逆运动学（IK）方法恢复了关节的参数。尽管问题定义明确且维度低，但必须迅速解决，这是多种可能的解决方案。在这项工作中，我们提出了一种神经IK方法，该方法采用问题的层次结构来顺序样本有效的关节角度，该角度在所需的位置和沿链的前面关节上进行条件。在我们的解决方案中，超级网$ f $恢复了多个主要网络的参数{$ g_1，g_2，\ dots，g_n $，其中$ n $是关节的数量}，因此每个$ g_i $均可输出可能的关节角度分布，并根据先前的原始网络的采样值来调节。可以在不观察多个解决方案的情况下，可以通过随时可用的匹配关节角度和位置对匹配的关节角度和位置进行训练。在测试时，通过依次从主要网络进行取样，提出了高变化的关节分布。与其他IK方法相比，我们证明了所提出的方法的优势，以及遵循笛卡尔空间中最终效应子的路径。
Inverse kinematic (IK) methods recover the parameters of the joints, given the desired position of selected elements in the kinematic chain. While the problem is well-defined and low-dimensional, it has to be solved rapidly, accounting for multiple possible solutions. In this work, we propose a neural IK method that employs the hierarchical structure of the problem to sequentially sample valid joint angles conditioned on the desired position and on the preceding joints along the chain. In our solution, a hypernetwork $f$ recovers the parameters of multiple primary networks {$g_1,g_2,\dots,g_N$, where $N$ is the number of joints}, such that each $g_i$ outputs a distribution of possible joint angles, and is conditioned on the sampled values obtained from the previous primary networks $g_j, j<i$. The hypernetwork can be trained on readily available pairs of matching joint angles and positions, without observing multiple solutions. At test time, a high-variance joint distribution is presented, by sampling sequentially from the primary networks. We demonstrate the advantage of the proposed method both in comparison to other IK methods for isolated instances of IK and with regard to following the path of the end effector in Cartesian space.
