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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.02046 |
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| _version_ | 1866918477634207744 |
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| author | Luo, Zhihao Yan, Wentao Gong, Jingyu Wang, Min Zhang, Zhizhong Wang, Xuhong Xie, Yuan Tan, Xin |
| author_facet | Luo, Zhihao Yan, Wentao Gong, Jingyu Wang, Min Zhang, Zhizhong Wang, Xuhong Xie, Yuan Tan, Xin |
| contents | Recent advances in Graphical User Interface (GUI) and embodied navigation have driven progress, yet these domains have largely evolved in isolation, with disparate datasets and training paradigms. In this paper, we observe that both tasks can be formulated as Markov Decision Processes (MDP), suggesting a foundational principle for their unification. Hence, we present NaviMaster, the first unified agent capable of unifying GUI navigation and embodied navigation within a single framework. Specifically, NaviMaster (i) proposes a visual-target trajectory collection pipeline that generates trajectories for both GUI and embodied tasks using a single formulation. (ii) employs a unified reinforcement learning framework on the mix data to improve generalization. (iii) designs a novel distance-aware reward to ensure efficient learning from the trajectories. Through extensive experiments on out-of-domain benchmarks, NaviMaster is shown to outperform state-of-the-art agents in GUI navigation, spatial affordance prediction, and embodied navigation. Ablation studies further demonstrate the efficacy of our unified training strategy, data mixing strategy, and reward design. Our codes, data, and checkpoints are available at https://iron-boyy.github.io/navimaster-page/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_02046 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NaviMaster: Learning a Unified Policy for GUI and Embodied Navigation Tasks Luo, Zhihao Yan, Wentao Gong, Jingyu Wang, Min Zhang, Zhizhong Wang, Xuhong Xie, Yuan Tan, Xin Robotics Machine Learning Recent advances in Graphical User Interface (GUI) and embodied navigation have driven progress, yet these domains have largely evolved in isolation, with disparate datasets and training paradigms. In this paper, we observe that both tasks can be formulated as Markov Decision Processes (MDP), suggesting a foundational principle for their unification. Hence, we present NaviMaster, the first unified agent capable of unifying GUI navigation and embodied navigation within a single framework. Specifically, NaviMaster (i) proposes a visual-target trajectory collection pipeline that generates trajectories for both GUI and embodied tasks using a single formulation. (ii) employs a unified reinforcement learning framework on the mix data to improve generalization. (iii) designs a novel distance-aware reward to ensure efficient learning from the trajectories. Through extensive experiments on out-of-domain benchmarks, NaviMaster is shown to outperform state-of-the-art agents in GUI navigation, spatial affordance prediction, and embodied navigation. Ablation studies further demonstrate the efficacy of our unified training strategy, data mixing strategy, and reward design. Our codes, data, and checkpoints are available at https://iron-boyy.github.io/navimaster-page/. |
| title | NaviMaster: Learning a Unified Policy for GUI and Embodied Navigation Tasks |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2508.02046 |