本研究以七自由度機械手結合影像與機器學習應用於抓取。傳統上需利用多組方程式描述抓取的動態與模型，進而尋找適當夾取點，然而本研究利用機器學習來找尋最佳夾取點，免除數學模擬與實務上的差距。本系統以機器學習的方式，辨識物品並找出目標物，接著建立目標物在三維空間的位置與姿態之模型，再以物體三維空間資訊做為資料，以強化學習的方式，學習機械手臂最佳的夾取位置與姿態。而本研究所用之機器學習分為三大部分，分別為物體辨識、形心與姿態估測，以及最佳抓取點系統，其中前兩部分採用卷積神經網路(Convolutional Neural Network)做為學習之架構，而最佳抓取點系統使用深度Q網絡(Deep Q Network)決定抓取策略。在機械手臂的部分，本系統使用六自由度的機械手臂加上一自由度的欠致動自適應性夾爪(underactuated adaptive gripper)，此自適應性夾爪可針對各式外型物品自動轉換成平行式與張角式夾爪，進行彈性化夾取。

This study uses a 7-DOF robotic arm combined with image and machine learning for grasping. Traditionally, multiple sets of equations are needed to describe the dynamics and models of the grabs, and then to find the appropriate gripping points. This system uses machine learning to find the best gripping points, eliminate the gap between mathematical simulation and practice. First, we use machine learning to identify the object and find the target. Second, the system builds the model of the centroid and posture of the target in three-dimensional space. Finally, the system uses the three-dimensional information of the target as The data to reinforcement learning and learn the best gripping points. The machine learning used in this study is divided into three parts, object identification, centroid and posture estimation, and the best grab point system. The first two parts use the Convolutional Neural Network as the learning framework, and the best grab point system uses the Deep Q Network to determine the grasping strategy. In the part of the hardware, the system uses a 6-DOF robotic arm plus 1-DOF underactuated adaptive gripper, which automatically converts parallel and angled jaws for various types of items for elastic clamping.

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