Door Manipulation

Research on door manipulation spans diverse applications, from improving indoor localization systems by optimizing sensor placement near doorways to developing robots capable of autonomously opening various door types. Current efforts focus on learning-based approaches, employing deep reinforcement learning and convolutional neural networks to handle the variability in door designs and mechanisms, often incorporating adaptive position-force control for safe and efficient interaction. These advancements have implications for robotics, accessibility, and smart home technologies, improving both the accuracy of indoor positioning and the capabilities of autonomous systems in navigating real-world environments.