MobRT: A Digital Twin-Based Framework for Scalable Learning in Mobile Manipulation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908577467203584 |
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| author | Mei, Yilin Qiu, Peng Zhang, Wei Zhang, WenChao Song, Wenjie |
| author_facet | Mei, Yilin Qiu, Peng Zhang, Wei Zhang, WenChao Song, Wenjie |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_04592 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MobRT: A Digital Twin-Based Framework for Scalable Learning in Mobile Manipulation Mei, Yilin Qiu, Peng Zhang, Wei Zhang, WenChao Song, Wenjie Robotics 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. |
| title | MobRT: A Digital Twin-Based Framework for Scalable Learning in Mobile Manipulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2510.04592 |