论文标题
高饮用者的深度微分掌握计划者
Deep Differentiable Grasp Planner for High-DOF Grippers
论文作者
论文摘要
我们提出了一种端到端算法，用于培训深层神经网络以掌握新物体。我们的算法使用前向后自动分化方法构建抓握系统的所有必需组件，包括抓手的前向运动学，抓地力和目标对象之间的碰撞以及用于抓紧姿势的度量。特别是，我们表明，对于由神经网络产生的不精确的graSP定义了广义的Q1 GRASP度量，并且可以通过对诱导优化问题的敏感性分析来计算我们的广义Q1公制的衍生物。我们表明，可以从低质量的水密三角网格中有效计算（自）碰撞项的衍生物。总的来说，我们的算法允许在没有地面真相数据的无人监督模式下为高dof抓地力的Grasp姿势计算，或者使用小数据集以监督模式改善结果。我们的新学习算法显着简化了基于学习的掌握系统的数据准备，并导致在常见的3D形状数据集[7，49，26，25]上获得了较高的学习率，在物理硬件上取得了22％的成功率，并且在Q1 Grasp质量指标上获得了0.12的成功率。
We present an end-to-end algorithm for training deep neural networks to grasp novel objects. Our algorithm builds all the essential components of a grasping system using a forward-backward automatic differentiation approach, including the forward kinematics of the gripper, the collision between the gripper and the target object, and the metric for grasp poses. In particular, we show that a generalized Q1 grasp metric is defined and differentiable for inexact grasps generated by a neural network, and the derivatives of our generalized Q1 metric can be computed from a sensitivity analysis of the induced optimization problem. We show that the derivatives of the (self-)collision terms can be efficiently computed from a watertight triangle mesh of low-quality. Altogether, our algorithm allows for the computation of grasp poses for high-DOF grippers in an unsupervised mode with no ground truth data, or it improves the results in a supervised mode using a small dataset. Our new learning algorithm significantly simplifies the data preparation for learning-based grasping systems and leads to higher qualities of learned grasps on common 3D shape datasets [7, 49, 26, 25], achieving a 22% higher success rate on physical hardware and a 0.12 higher value on the Q1 grasp quality metric.
