本論文旨於提出一認知學習演算法，使得人形機器人在投球九宮格遊戲中能自主學習投球姿態，學習完成後機器人能夠準確地擊落九宮格上所有格板。本論文所有實驗建構於本實驗室研製之中型人形機器人David Junior上，其中影像處理與學習演算法兩大核心架構，均採用機器人身上之工業電腦為中央運算模組。在影像處理方面，運用一網路攝影機做為機器人的視覺感知器，另外在九宮格上方及側邊分別裝設一台網路攝影機，以利於捕捉投球的軌跡和落點。學習演算法則是提出模仿人類思考行為模式的認知學習演算法，其靈感係來自於2002年諾貝爾經濟學獎得主，心理學家-丹尼爾·康納曼所著“Thinking, Fast and Slow”一書，書中提到人類思考決策模式主要分成直覺式的快思系統，以及邏輯式的慢想系統。本論文以深度信賴網路來建立快思系統，以慣性權重粒子群最佳化演算法來建立慢想系統，藉由機器人投球學習得到此快思慢想的模型。最後經由實驗結果，可充分展現此認知演算法之可行性與卓越成效。

This thesis aims to design a cognition learning algorithm that allows the robot to learn the posture of playing 3 by 3 square baseball game. The robot can hit the designated grid area accurately with this proposed algorithm. The overall system proposed in this thesis includes image processing algorithm and learning algorithm. In the image processing system, a CMOS webcam sensor is used on the robot as the eye. In order to catch the ball location efficiently, two internet protocol cameras are installed on the top and side of the 3 by 3 square. To recognize and track the objects, a simple searching algorithm is developed for the issue. Then, a novel learning algorithm is motivated by a human thinking conception proposed in “Thinking, Fast and Slow” by Daniel Kahneman. He is a psychologist who won the Nobel Memorial Prize in Economic Science 2002. This algorithm is based on cognitive psychology, which divides human thinking into two modes, fast and slow. The fast mode favors intuitive thinking while the slow mode favors rational thinking. Furthermore, we establish the fast mode by Deep Belief Network and the slow mode by Inertia Weight Particle Swarm Optimization Algorithm in the developed cognition learning algorithm. The proposed algorithm is implemented and applied on the robot, and then the robot performs the fast and slow mode in 3 by 3 square baseball game. Finally, experimental results demonstrate that the performance of the cognition learning method is very efficient. In other words, this learning algorithm also verifies that the thinking mode of the human being is reasonable and available on the robot.

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