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.

处理关节物体的能力对于协助人类的机器人非常重要。在这项工作中，提出了使用自主移动操纵器来稳健地自适应操作普通门的框架。为了推动鲁棒性和速度性能的发展，我们设计了一种新颖的算法，该算法将卷积神经网络与有效的点云处理融合在一起。这项进步使得能够根据RGB-D图像对多个手柄进行实时抓握姿势估计，从而为以人为中心的辅助应用程序提供了加速改进。此外，我们提出了一种通用的贝叶斯框架，该框架使机器人能够从运动观察中推断出门运动模型，并可以从以前的经验或人类示范中学到东西。将这些算法与“任务空间区域”运动计划器结合使用，无论运动模型如何，我们都能实现高效的门操作。我们使用丰田人力支持机器人通过实际实验验证了我们的框架。