Learning by demonstration for tool-handling task

When the robot comes to the home-like environment, its programming becomes very demanding. The concept of learning by demonstration is thus introduced, which intends to remove the load of detailed analysis and programming from the user. Following this concept, in this paper, 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. 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. We apply the concept of cross-validation to locate the portions of the trajectory that correspond to delicate and skillful maneuvering, and apply an algorithm based on dynamic programming previously developed to search for the most probable intention. In experiments, we apply the proposed approach for two different kinds of tasks, the pouring and coffee-making tasks, with the number of objects and their locations varied during demonstrations.