A versatile door opening system with mobile manipulator through adaptive position-force control and reinforcement learning

• Published in: Robotics and autonomous systems Vol. 180; p. 104760
• Main Authors: Kang, Gyuree, Seong, Hyunki, Lee, Daegyu, Shim, David Hyunchul
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2024
• Subjects: Deep reinforcement learning; Door opening robot; Indoor robotics; Mobile manipulator; Real-time autonomous system; Door opening robot; Deep reinforcement learning; Indoor robotics; Real-time autonomous system; Mobile manipulator
• DOI: 10.1016/j.robot.2024.104760
• ISSN: 0921-8890

The ability of robots to navigate through doors is crucial for their effective operation in indoor environments. Consequently, extensive research has been conducted to develop robots capable of opening specific doors. However, the diverse combinations of door handles and opening directions necessitate a more versatile door opening system for robots to successfully operate in real-world environments. In this paper, we propose a mobile manipulator system that can autonomously open various doors without prior knowledge. By using convolutional neural networks, point cloud extraction techniques, and external force measurements during exploratory motion, we obtained information regarding handle types, poses, and door characteristics. Through two different approaches, adaptive position-force control and deep reinforcement learning, we successfully opened doors without precise trajectory or excessive external force. The adaptive position-force control method involves moving the end-effector in the direction of the door opening while responding compliantly to external forces, ensuring safety and manipulator workspace. Meanwhile, the deep reinforcement learning policy minimizes applied forces and eliminates unnecessary movements, enabling stable operation across doors with different poses and widths. The RL-based approach outperforms the adaptive position-force control method in terms of compensating for external forces, ensuring smooth motion, and achieving efficient speed. It reduces the maximum force required by 3.27 times and improves motion smoothness by 1.82 times. However, the non-learning-based adaptive position-force control method demonstrates more versatility in opening a wider range of doors, encompassing revolute doors with four distinct opening directions and varying widths. •Versatile mobile manipulator door opening system without prior knowledge.•Applicable to doors with various handle types and different kinematic constraints.•Adaptive position-force control-based and RL-based systems for adaptive door opening.•Extensive evaluation in real-world scenarios.