Probabilistic Mobile Manipulation in Dynamic Environments, with Application to Opening Doors

Anna Petrovskaya, Andrew Y. Ng

In recent years, probabilistic approaches have found many successful applications to mobile robot localization, and to object state estimation for manipulation. In this paper, we propose a unified approach to these two problems that dynamically models the objects to be manipulated and localizes the robot at the same time. Our approach applies in the common setting where only a low-resolution (10cm) grid-map of a building is available, but we also have a high-resolution (0.1cm) model of the object to be manipulated. Our method is based on defining a unifying probabilistic model over these two representations. The resulting algorithm works in real-time, and estimates the position of objects with sufficient precision for manipulation tasks. We apply our approach to the task of navigating from one office to another (including manipulating doors). Our approach, successfully tested on multiple doors, allows the robot to navigate through a hallway to an office door, grasp and turn the door handle, and continuously manipulate the door as it moves into the office.

URL: http://cs.stanford.edu/~anya/stair/