论文标题
Lyrn（Lyapunov到达网络）：单眼视觉的实时闭环方法
LyRN (Lyapunov Reaching Network): A Real-Time Closed Loop approach from Monocular Vision
论文作者
论文摘要
我们提出了一种基于新颖的学习原理的闭环，多实体控制算法，用于视觉引导。如果可以使用完整的状态信息（所有潜在的到达点的姿势），则使用控制Lyapunov函数方法来为复杂的多企业任务设计到达动作。所提出的算法使用单眼视觉和操纵器关节角作为深卷卷积神经网络的输入，以预测控制Lyapunov函数（CLF）和相应的速度控制的值。由Control Lyapunov函数的设计自然出现了自然出现的掌握功能，将最终的网络输出用作掌握任务的视觉控制。 我们演示了提出的算法握住杯子（无纹理和对称对象），上面是从肩膀单眼RGB摄像头上的桌上顶上。操纵器从工作区内的任何随机初始姿势中的多个相同实例中动态收敛到最适合的目标。 The system trained with only simulated data is able to achieve 90.3% grasp success rate in the real-world experiments with up to 85Hz closed-loop control on one GTX 1080Ti GPU and significantly outperforms a Pose-Based-Visual-Servo (PBVS) grasping system adapted from a state-of-the-art single shot RGB 6D pose estimation algorithm.该论文的一个关键贡献是在学习过程中包含与CLF作为正规化项相关的一阶差分约束，并且证据表明，这会导致比一般控制输入的香草回归相比，这会导致更健壮和可靠的达到/抓地力。
We propose a closed-loop, multi-instance control algorithm for visually guided reaching based on novel learning principles. A control Lyapunov function methodology is used to design a reaching action for a complex multi-instance task in the case where full state information (poses of all potential reaching points) is available. The proposed algorithm uses monocular vision and manipulator joint angles as the input to a deep convolution neural network to predict the value of the control Lyapunov function (cLf) and corresponding velocity control. The resulting network output is used in real-time as visual control for the grasping task with the multi-instance capability emerging naturally from the design of the control Lyapunov function. We demonstrate the proposed algorithm grasping mugs (textureless and symmetric objects) on a table-top from an over-the-shoulder monocular RGB camera. The manipulator dynamically converges to the best-suited target among multiple identical instances from any random initial pose within the workspace. The system trained with only simulated data is able to achieve 90.3% grasp success rate in the real-world experiments with up to 85Hz closed-loop control on one GTX 1080Ti GPU and significantly outperforms a Pose-Based-Visual-Servo (PBVS) grasping system adapted from a state-of-the-art single shot RGB 6D pose estimation algorithm. A key contribution of the paper is the inclusion of a first-order differential constraint associated with the cLf as a regularisation term during learning, and we provide evidence that this leads to more robust and reliable reaching/grasping performance than vanilla regression on general control inputs.
