{"ID":2859757,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.04592","arxiv_id":"2510.04592","title":"MobRT: A Digital Twin-Based Framework for Scalable Learning in Mobile Manipulation","abstract":"Recent advances in robotics have been largely driven by imitation learning, which depends critically on large-scale, high-quality demonstration data. However, collecting such data remains a significant challenge-particularly for mobile manipulators, which must coordinate base locomotion and arm manipulation in high-dimensional, dynamic, and partially observable environments. Consequently, most existing research remains focused on simpler tabletop scenarios, leaving mobile manipulation relatively underexplored. To bridge this gap, we present \\textit{MobRT}, a digital twin-based framework designed to simulate two primary categories of complex, whole-body tasks: interaction with articulated objects (e.g., opening doors and drawers) and mobile-base pick-and-place operations. \\textit{MobRT} autonomously generates diverse and realistic demonstrations through the integration of virtual kinematic control and whole-body motion planning, enabling coherent and physically consistent execution. We evaluate the quality of \\textit{MobRT}-generated data across multiple baseline algorithms, establishing a comprehensive benchmark and demonstrating a strong correlation between task success and the number of generated trajectories. Experiments integrating both simulated and real-world demonstrations confirm that our approach markedly improves policy generalization and performance, achieving robust results in both simulated and real-world environments.","short_abstract":"Recent advances in robotics have been largely driven by imitation learning, which depends critically on large-scale, high-quality demonstration data. However, collecting such data remains a significant challenge-particularly for mobile manipulators, which must coordinate base locomotion and arm manipulation in high-dim...","url_abs":"https://arxiv.org/abs/2510.04592","url_pdf":"https://arxiv.org/pdf/2510.04592v1","authors":"[\"Yilin Mei\",\"Peng Qiu\",\"Wei Zhang\",\"WenChao Zhang\",\"Wenjie Song\"]","published":"2025-10-06T08:46:56Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}