# Anthropomorphic Grasping of Complex-Shaped Objects Using Imitation Learning
## 1 Introduction  
Although robotic manipulators have been widely used for decades, their roles have been mostly limited to repetitive tasks in industrial environments due to safety reasons. However, with the widespread adoption of cooperative robots that can safely work in human environments, and the growing demand for small-quantity batch productions, we now see an increasing number of smart, flexible robotic manipulation systems used for many different tasks [1; 2; 3]. Furthermore, with recent advances in machine learning algorithms, with the help of low-cost, high-resolution depth sensors, now many robotic manipulation systems can perceive environments and manipulate objects utilizing sensory information to intelligently handle complex nonrepetitive tasks, such as bin picking [4; 5].  
In addition to perception, there have been approaches to utilize machine learning to optimize the motions of complex, high-DOF robotic systems. However, as it is often not practical to prepare a large amount of training data using physical robotic systems, they require highly data-efficient machine learning algorithms. One such approach is imitation learning, which uses a small number of demonstration data from human experts to quickly learn the desirable motion policy for the given tasks [6; 7; 8; 9; 10; 11; 12]. Although human-like robotic hands have distinctive advantages in robotic manipulation over simple robotic grippers, such as a large number of grasp types [13] and in-hand manipulation capability [14], there have been few previous works that use anthropomorphic robotic hand forautonomous robotic manipulation due to difficulties coming from the high number of DOFs of the system.  
This work presents a robotic platform that autonomously perceives and grasps complex-shaped objects using an anthropomorphic robotic hand. Unlike most of the previous approaches that utilize imitation learning for robotic manipulation, our approach learns how to utilize a high-DOF human-like robotic hand for manipulation using a number of machine learning approaches, which includes ResNet [15], KNN [16], point-set registration [17; 18], and dynamical movement primitives (DMPs) [19]. As the direct teaching method cannot be used for such a high-DOF system, we use a VR controller with full finger tracking to acquire each finger motion data as well as the 6D trajectory of the hand. The suggested system can learn the grasp pose, hand trajectory, and finger motions for grasping complex-shaped objects from a limited number of demonstrations and is capable of reliably grasping such objects with arbitrary, previously unknown orientations.  
The remainder of the paper proceeds as follows. Section 3 introduces the hardware configuration used for the work, which is shown in Figure 1. Section 4 describes the overall workflow of perception, pose and class estimation, motion planning, and grasping an object. Section 5 presents how the training data are collected, which consists of 3D point clouds, 6D hand trajectory data, and RGB images of the objects. Section 6 explains how the platform estimates the grasping pose for the manipulation object utilizing the learning data collected in Section 5. Section 7 shows the hand trajectory generation process given the grasping pose. Finally, Section 8 presents the experimental results to validate the system's performance done by an official tester. Finally, we conclude the future direction of this work in Section 9.