Robust and adaptive door operation with a mobile robot

Journal Article (2021)


Intelligent Service Robotics







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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.


humanoid robots, intelligent robots, manipulators, pattern recognition, service robots.

Author keywords

Handle grasping, Door operation, Kinematic model learning, Task space region

Scientific reference

M. Arduengo, C. Torras and L. Sentis. Robust and adaptive door operation with a mobile robot. Intelligent Service Robotics, 14(3): 409-425, 2021.