Operating articulated objects based on experience

©2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.DOI:10.1109/IROS.2010.5653813Presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2010, Taipei, 18-22 October 2010.Many tasks that would be of benefit to users in domestic environments require that robots manipulate articulated objects such as doors and drawers. In this paper, we present a novel approach that simultaneously estimates the kinematic model of an articulated object based on the trajectory described by the robot's end effector, and uses this model to predict the future trajectory of the end effector. One advantage of our approach is that the robot can directly use these predictions to generate an equilibrium point control path for operating the mechanism. Additionally, our approach can improve these predictions based on previously learned articulation models. We have implemented and tested our approach on a real mobile manipulator. Through 40 trials, we show that the robot can reliably open various household objects, including cabinet doors, sliding doors, office drawers, and a dishwasher. Furthermore, we demonstrate that using the information from previous interactions as a prior significantly improves the prediction accuracy