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.

翻译：机器人具备自主开门能力对其在室内环境中的有效运行至关重要。为此，国内外学者已开展大量研究，开发出能开启特定类型门的机器人。然而，由于门把手类型与开门方向的多样组合，机器人需要在真实环境中实现更通用的开门系统才能成功作业。本文提出一种无需先验知识即可自主开启多种类型门的移动机械臂系统。通过卷积神经网络、点云提取技术以及探索运动中测量的外力信息，我们获取了把手类型、位姿及门体特性参数。采用自适应力位控制与深度强化学习两种不同方法，我们在无需精确轨迹规划且不产生过大外力的情况下成功完成了开门操作。自适应力位控制方法通过使末端执行器沿开门方向运动并对外力进行柔顺响应，确保了操作安全性与机械臂工作空间适应性。深度强化学习策略则通过最小化施加力并消除无效运动，实现了对不同位姿与宽度门体的稳定操作。在补偿外力、保障运动平滑性及提升效率方面，基于强化学习的方法优于自适应力位控制方法：将最大作用力降低3.27倍，运动平滑度提升1.82倍。但无学习机制的自适应力位控制方法在开启更广泛门体类型方面展现出更强的通用性，可覆盖四种不同开门方向与可变宽度的旋转门。