RUMOR: Reinforcement learning for understanding a model of the real world for navigation in dynamic environments
Martinez-Baselga, Diego (Universidad de Zaragoza) ; Riazuelo, Luis (Universidad de Zaragoza) ; Montano, Luis (Universidad de Zaragoza)
Resumen: Autonomous navigation in dynamic environments is a complex but essential task for autonomous robots, with recent deep reinforcement learning approaches showing promising results. However, the complexity of the real world makes it infeasible to train agents in every possible scenario configuration. Moreover, existing methods typically overlook factors such as robot kinodynamic constraints, or assume perfect knowledge of the environment. In this work, we present RUMOR, a novel planner for differential-drive robots that uses deep reinforcement learning to navigate in highly dynamic environments. Unlike other end-to-end DRL planners, it uses a descriptive robocentric velocity space model to extract the dynamic environment information, enhancing training effectiveness and scenario interpretation. Additionally, we propose an action space that inherently considers robot kinodynamics and train it in a simulator that reproduces the real world problematic aspects, reducing the gap between the reality and simulation. We extensively compare RUMOR with other state-of-the-art approaches, demonstrating a better performance, and provide a detailed analysis of the results. Finally, we validate RUMOR’s performance in real-world settings by deploying it on a ground robot. Our experiments, conducted in crowded scenarios and unseen environments, confirm the algorithm’s robustness and transferability.
Idioma: Inglés
DOI: 10.1016/j.robot.2025.105020
Año: 2025
Publicado en: ROBOTICS AND AUTONOMOUS SYSTEMS 191 (2025), 105020 [12 pp.]
ISSN: 0921-8890
Financiación: info:eu-repo/grantAgreement/ES/AEI/PID2022-139615OB-I00
Financiación: info:eu-repo/grantAgreement/ES/AEI/PRE2020-094415
Financiación: info:eu-repo/grantAgreement/ES/DGA/T45-23R
Tipo y forma: Article (Published version)
Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes.
Exportado de SIDERAL (2025-10-17-14:18:57)
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Idioma: Inglés
DOI: 10.1016/j.robot.2025.105020
Año: 2025
Publicado en: ROBOTICS AND AUTONOMOUS SYSTEMS 191 (2025), 105020 [12 pp.]
ISSN: 0921-8890
Financiación: info:eu-repo/grantAgreement/ES/AEI/PID2022-139615OB-I00
Financiación: info:eu-repo/grantAgreement/ES/AEI/PRE2020-094415
Financiación: info:eu-repo/grantAgreement/ES/DGA/T45-23R
Tipo y forma: Article (Published version)
Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. Exportado de SIDERAL (2025-10-17-14:18:57)
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Record created 2025-05-30, last modified 2025-10-17