Robust and Adaptive Door Operation with a Mobile Robot
This work addresses the challenge of robots dealing with articulated objects like doors for assistive human-centered applications, representing an incremental improvement in robustness and speed.
The paper tackles the problem of enabling a mobile robot to robustly and adaptively operate common doors by proposing a framework that fuses a convolutional neural network with point cloud processing for real-time grasping pose estimation and a Bayesian framework for inferring door kinematic models, achieving efficient door operation validated with real-world experiments on the Toyota Human Support Robot.
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.