Intention deduction from demonstrated trajectory for tool-handling task

Abstract

When the robot comes to a home-like environment, its programming becomes very demanding. The concept of learning by demonstration is thus introduced, which may remove the load of detailed analysis and programming from the user. Following this concept, in this article, 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 allowing for the presence of redundant operations during the 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 applied 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. To further investigate our method's scalability and generality, we also performed intensive analysis on the parameters involved in the tasks.