隨著科技發展，機器人不再是只有工業上的應用，有愈來愈多的服務型機器人已逐漸地融入在我們的日常生活當中，為各行各業的工作者提供協助。但是，執行利用視覺感測進行物件操作的任務對於服務型機器人仍是一大難關，因為像這樣的控制演算法通常需要透過非常大量的標註資料進行訓練，而且時常會需要針對模型未見過的物體進行額外標註，這對於一般未接觸過人工智慧的使用者無疑是一大困擾，也間接增加了機器人與潛在消費者市場的隔閡。為了使機器人能夠更妥善地應對日常環境中各種可能遇到的使用情境，並節省掉額外訓練資料的標註成本，勢必需要一個能自動適應任意環境，並且不需要標註訓練資料就能進行學習的通用物件夾取演算法。
有鑑於此，本研究開發一套基於視覺的深度強化學習機械手臂夾取系統，透過深度學習與強化學習的結合，讓機器人能自動根據環境提供的視覺資訊以及回饋值來進行學習，其中不需要任何人工或非人工的標註資料，可以節省掉大量的訓練資料標註人力與時間成本。經實驗測試，本研究所提出的多重動作輸出深度Q網路可以有效的減少訓練時程，並且可以成功夾取未出現在訓練階段的物體。

With the development of technology, robots are no longer confined to industrial applications. An increasing number of service robots have gradually integrated into our daily lives, providing assistance to professionals in various fields. However, performing tasks that involve object manipulation using visual sensing remains a significant challenge for service robots. This is because control algorithms of this nature often require extensive annotated data for training and may need additional annotations for objects unseen by the model. This undoubtedly poses a major inconvenience for users who are unfamiliar with artificial intelligence and creates a gap between robots and potential consumer markets.
To enable robots to handle various scenarios in everyday environments more effectively and to reduce the cost of annotating additional training data, a generic object-grasping algorithm that can adapt automatically to any environment without the need for annotated training data is essential.
In light of this, this research develops a visual-based deep reinforcement learning robotic arm grasping system. By combining deep learning and reinforcement learning, the robot can learn automatically based on the visual information and feedback reward provided by the environment, without requiring any manual or non-manual annotation data. This approach saves a significant amount of human and time costs involved in data annotation for training. Through experimental testing, the proposed multi-action output deep Q network in this research can effectively reduce the training time and successfully grasp objects not seen during the training phase.

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