Local contextual trajectory estimation with demonstration for assisting mobile robot teleoperation

We focus on assisting mobile robot teleoperation in a task-appropriate way, where we model the user intention as an action primitive to perform a contextual task, e.g. doorway crossing and object inspection, and provide motion assistance according to the task recognition. This paper contributes to formulating motion assistance in a data-driven manner. With the motion clusters obtained in our previous report [1], we apply a fast online Gaussian Mixture Regression (GMR) approach to the most probable motion cluster classified during operation, to estimate the local trajectory the human user intends to follow in the short term for the corresponding task execution with the recognized contextual information. To regulate the estimation accuracy, we compute the Mahalanobis distance of each estimated trajectory way point. By thresholding the distance, we can achieve the trajectory estimation within a pre-defined tolerance bound regarding the regression outliers. The experimental results from both the qualitative and quantitative tests using the real data confirmed the effectiveness and real-time performance of the proposed approach.