Progress-Think: Semantic Progress Reasoning for Vision-Language Navigation
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arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911592700968960 |
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| author | Wang, Shuo Wang, Yucheng Lian, Guoxin Wang, Yongcai Chen, Maiyue Wang, Kaihui Zhang, Bo Su, Zhizhong Zhou, Yutian Li, Wanting Li, Deying Fan, Zhaoxin |
| author_facet | Wang, Shuo Wang, Yucheng Lian, Guoxin Wang, Yongcai Chen, Maiyue Wang, Kaihui Zhang, Bo Su, Zhizhong Zhou, Yutian Li, Wanting Li, Deying Fan, Zhaoxin |
| contents | Vision-Language Navigation requires agents to act coherently over long horizons by understanding not only local visual context but also how far they have advanced within a multi-step instruction. However, recent Vision-Language-Action models focus on direct action prediction and earlier progress methods predict numeric achievements; both overlook the monotonic co-progression property of the observation and instruction sequences. Building on this insight, Progress-Think introduces semantic progress reasoning, predicting instruction-style progress from visual observations to enable more accurate navigation. To achieve this without expensive annotations, we propose a three-stage framework. In the initial stage, Self-Aligned Progress Pretraining bootstraps a reasoning module via a novel differentiable alignment between visual history and instruction prefixes. Then, Progress-Guided Policy Pretraining injects learned progress states into the navigation context, guiding the policy toward consistent actions. Finally, Progress-Policy Co-Finetuning jointly optimizes both modules with tailored progress-aware reinforcement objectives. Experiments on R2R-CE and RxR-CE show state-of-the-art success and efficiency, demonstrating that semantic progress yields a more consistent representation of navigation advancement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_17097 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Progress-Think: Semantic Progress Reasoning for Vision-Language Navigation Wang, Shuo Wang, Yucheng Lian, Guoxin Wang, Yongcai Chen, Maiyue Wang, Kaihui Zhang, Bo Su, Zhizhong Zhou, Yutian Li, Wanting Li, Deying Fan, Zhaoxin Robotics Vision-Language Navigation requires agents to act coherently over long horizons by understanding not only local visual context but also how far they have advanced within a multi-step instruction. However, recent Vision-Language-Action models focus on direct action prediction and earlier progress methods predict numeric achievements; both overlook the monotonic co-progression property of the observation and instruction sequences. Building on this insight, Progress-Think introduces semantic progress reasoning, predicting instruction-style progress from visual observations to enable more accurate navigation. To achieve this without expensive annotations, we propose a three-stage framework. In the initial stage, Self-Aligned Progress Pretraining bootstraps a reasoning module via a novel differentiable alignment between visual history and instruction prefixes. Then, Progress-Guided Policy Pretraining injects learned progress states into the navigation context, guiding the policy toward consistent actions. Finally, Progress-Policy Co-Finetuning jointly optimizes both modules with tailored progress-aware reinforcement objectives. Experiments on R2R-CE and RxR-CE show state-of-the-art success and efficiency, demonstrating that semantic progress yields a more consistent representation of navigation advancement. |
| title | Progress-Think: Semantic Progress Reasoning for Vision-Language Navigation |
| topic | Robotics |
| url | https://arxiv.org/abs/2511.17097 |