Print Email Facebook Twitter Visual Navigation in Real-World Indoor Environments Using End-to-End Deep Reinforcement Learning Title Visual Navigation in Real-World Indoor Environments Using End-to-End Deep Reinforcement Learning Author Kulhanek, Jonas (Czech Technical University) Derner, Erik (Czech Technical University) Babuska, R. (TU Delft Learning & Autonomous Control; Czech Technical University) Date 2021 Abstract Visual navigation is essential for many applications in robotics, from manipulation, through mobile robotics to automated driving. Deep reinforcement learning (DRL) provides an elegant map-free approach integrating image processing, localization, and planning in one module, which can be trained and therefore optimized for a given environment. However, to date, DRL-based visual navigation was validated exclusively in simulation, where the simulator provides information that is not available in the real world, e.g., the robot's position or segmentation masks. This precludes the use of the learned policy on a real robot. Therefore, we present a novel approach that enables a direct deployment of the trained policy on real robots. We have designed a new powerful simulator capable of domain randomization. To facilitate the training, we propose visual auxiliary tasks and a tailored reward scheme. The policy is fine-tuned on images collected from real-world environments. We have evaluated the method on a mobile robot in a real office environment. The training took approximately 30 hours on a single GPU. In 30 navigation experiments, the robot reached a 0.3-meter neighbourhood of the goal in more than 86.7% of cases. This result makes the proposed method directly applicable to tasks like mobile manipulation. Subject Camerasdeep learning methodsNavigationreinforcement learningReinforcement learningRobotsTask analysisTrainingVision-based navigationVisualization To reference this document use: http://resolver.tudelft.nl/uuid:590c6c75-d3e9-4553-b54c-d4c7609ff2d9 DOI https://doi.org/10.1109/LRA.2021.3068106 ISSN 2377-3766 Source IEEE Robotics and Automation Letters, 6 (3), 4345-4352 Part of collection Institutional Repository Document type journal article Rights © 2021 Jonas Kulhanek, Erik Derner, R. Babuska Files PDF 09384194.pdf 4.24 MB Close viewer /islandora/object/uuid:590c6c75-d3e9-4553-b54c-d4c7609ff2d9/datastream/OBJ/view