  The ability to deal with articulated objects is very important for robots
assisting humans. In this work, a framework to robustly and adaptively operate
common doors, using an autonomous mobile manipulator, is proposed. To push
forward the state-of-the-art in robustness and speed performance, we devise a
novel algorithm that fuses a convolutional neural network with efficient point
cloud processing. This advancement enables real-time grasping pose estimation
for multiple handles from RGB-D images, providing a speed up improvement for
assistive human-centered applications. In addition, we propose a versatile
Bayesian framework that endows the robot with the ability to infer the door
kinematic model from observations of its motion and learn from previous
experiences or human demonstrations. Combining these algorithms with a Task
Space Region motion planner, we achieve an efficient door operation regardless
of the kinematic model. We validate our framework with real-world experiments
using the Toyota Human Support Robot.
