{"ID":2884459,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.07079","arxiv_id":"2508.07079","title":"Model Predictive Control for Crowd Navigation via Learning-Based Trajectory Prediction","abstract":"Safe navigation in pedestrian-rich environments remains a key challenge for autonomous robots. This work evaluates the integration of a deep learning-based Social-Implicit (SI) pedestrian trajectory predictor within a Model Predictive Control (MPC) framework on the physical Continental Corriere robot. Tested across varied pedestrian densities, the SI-MPC system is compared to a traditional Constant Velocity (CV) model in both open-loop prediction and closed-loop navigation. Results show that SI improves trajectory prediction - reducing errors by up to 76% in low-density settings - and enhances safety and motion smoothness in crowded scenes. Moreover, real-world deployment reveals discrepancies between open-loop metrics and closed-loop performance, as the SI model yields broader, more cautious predictions. These findings emphasize the importance of system-level evaluation and highlight the SI-MPC framework's promise for safer, more adaptive navigation in dynamic, human-populated environments.","short_abstract":"Safe navigation in pedestrian-rich environments remains a key challenge for autonomous robots. This work evaluates the integration of a deep learning-based Social-Implicit (SI) pedestrian trajectory predictor within a Model Predictive Control (MPC) framework on the physical Continental Corriere robot. Tested across var...","url_abs":"https://arxiv.org/abs/2508.07079","url_pdf":"https://arxiv.org/pdf/2508.07079v1","authors":"[\"Mohamed Parvez Aslam\",\"Bojan Derajic\",\"Mohamed-Khalil Bouzidi\",\"Sebastian Bernhard\",\"Jan Oliver Ringert\"]","published":"2025-08-09T19:11:28Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\",\"eess.SY\"]","methods":"[]","has_code":false}