Enabling Service Robots to Open Self-Closing Doors using Deep RL and Generative Models

Various mobile service robots have been applied in human-centered indoor environments such as hospitals, schools, and office buildings for tasks like food or material delivery, surveillance, cleaning, and disinfection. However, they still require human assistance to access rooms with doors, particularly self-closing doors. In this paper, we propose a deep reinforcement learning (DRL)-based solution to enhance existing service robots, enabling them to autonomously open self-closing doors using a simple and inexpensive assistive device. This device is utilized to unlatch doors through the integration of the latest object detection and sensor technologies. Focusing on pulling open self-closing doors, we train an end-to-end control policy in a simplified simulation environment and deploy it to a real-world robot. Additionally, generative models, a variational autoenoder (VAE) and a cycle-consistent adversarial network (CycleGAN), are incorporated into the training and deployment stages to improve the robustness of the door-opening control policy. The proposed solution for opening self-closing doors was validated in both simulation and real-world experiments.