基於人類示範之意圖推論於工具操作任務之應用

Abstract

在不久的將來，可以預期越來越多的機器人工作環境會從工廠移至家庭環境，如果機器人在面對各種家庭任務時均需有對應的程式是不切實際的。 因此，有學者提出讓機器人從示範中學習的概念，可以減少使用者分析與程式的負擔。 然而許多遵循這概念的方法卻需要限制使用者動作或任務計畫進而得以推論使用者的意圖。 為了避免這些限制，我們提出一個新的方法讓機器人能從工具操作任務執行的軌跡中推論出示範者的意圖。 在家庭環境中，工具操作任務是常見的任務，但是分析示範者的意圖卻不容易。 我們的方法乃基於交叉驗證的概念，定位出符合精細技巧操作的軌跡片段，並且利用動態規劃尋找最有可能的意圖。 我們提出的方法不需要事先定義可操作的動作或限制動作的速度，並且在示範的過程中允許變換動作順序，也可加入多餘的動作。 在實驗中，我們提出的方法以三種任務進行測試，分別是倒水、泡咖啡及塗果醬任務，在示範中改變任務物件的位置和數目以測試其影響。 更進一步，我們分析任務中各參數的影響來研究方法的適用性。 實驗結果顯示我們的方法不但對於這三種任務可以推論使用者的意圖，而且可以讓使用者在沒有限制的情況下以較自然且有效率的方式示範動作。

In the near future, more robots come to the home-like environment, the programming for task execution becomes very demanding, if not infeasible. The concept of learning from demonstration is thus introduced, which may remove the load of detailed analysis and programming from the user. However, many methods which follow the concept of learning from demonstration limit the motions of the user or task plan to deduce the intentions of the user. To avoid these limitations, in this dissertation, we propose a novel approach for the robot to deduce the intention of the demonstrator from the trajectories during task execution. We focus on the tool-handling task, which is common in the home environment, but complicated for analysis. We apply the concept of cross-validation to locate the portions of the trajectory that corresponds to delicate and skillful maneuvering, and apply an algorithm based on dynamic programming to search for the most probable intention. The proposed approach does not predefine motions or put constraints on motion speed, while allowing the event order to be altered and the presence of redundant operations during demonstration. In experiments, we apply the proposed approach for three different kinds of tasks: pouring, coffee-making, and fruit jam, with the number of objects and their locations varied during demonstrations. To further investigate its scalability and generality, we also perform intensive analysis on the parameters involved in the tasks. The results show that our approach can not only deduce the intentions of user in the three kinds of tasks but also let the demonstrations be executed in a natural and effective manner without the limitations.