Long-VLA: Unleashing Long-Horizon Capability of Vision Language Action Model for Robot Manipulation
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arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866915467564679168 |
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| author | Fan, Yiguo Ding, Pengxiang Bai, Shuanghao Tong, Xinyang Zhu, Yuyang Lu, Hongchao Dai, Fengqi Zhao, Wei Liu, Yang Huang, Siteng Fan, Zhaoxin Chen, Badong Wang, Donglin |
| author_facet | Fan, Yiguo Ding, Pengxiang Bai, Shuanghao Tong, Xinyang Zhu, Yuyang Lu, Hongchao Dai, Fengqi Zhao, Wei Liu, Yang Huang, Siteng Fan, Zhaoxin Chen, Badong Wang, Donglin |
| contents | Vision-Language-Action (VLA) models have become a cornerstone in robotic policy learning, leveraging large-scale multimodal data for robust and scalable control. However, existing VLA frameworks primarily address short-horizon tasks, and their effectiveness on long-horizon, multi-step robotic manipulation remains limited due to challenges in skill chaining and subtask dependencies. In this work, we introduce Long-VLA, the first end-to-end VLA model specifically designed for long-horizon robotic tasks. Our approach features a novel phase-aware input masking strategy that adaptively segments each subtask into moving and interaction phases, enabling the model to focus on phase-relevant sensory cues and enhancing subtask compatibility. This unified strategy preserves the scalability and data efficiency of VLA training, and our architecture-agnostic module can be seamlessly integrated into existing VLA models. We further propose the L-CALVIN benchmark to systematically evaluate long-horizon manipulation. Extensive experiments on both simulated and real-world tasks demonstrate that Long-VLA significantly outperforms prior state-of-the-art methods, establishing a new baseline for long-horizon robotic control. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19958 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Long-VLA: Unleashing Long-Horizon Capability of Vision Language Action Model for Robot Manipulation Fan, Yiguo Ding, Pengxiang Bai, Shuanghao Tong, Xinyang Zhu, Yuyang Lu, Hongchao Dai, Fengqi Zhao, Wei Liu, Yang Huang, Siteng Fan, Zhaoxin Chen, Badong Wang, Donglin Robotics Vision-Language-Action (VLA) models have become a cornerstone in robotic policy learning, leveraging large-scale multimodal data for robust and scalable control. However, existing VLA frameworks primarily address short-horizon tasks, and their effectiveness on long-horizon, multi-step robotic manipulation remains limited due to challenges in skill chaining and subtask dependencies. In this work, we introduce Long-VLA, the first end-to-end VLA model specifically designed for long-horizon robotic tasks. Our approach features a novel phase-aware input masking strategy that adaptively segments each subtask into moving and interaction phases, enabling the model to focus on phase-relevant sensory cues and enhancing subtask compatibility. This unified strategy preserves the scalability and data efficiency of VLA training, and our architecture-agnostic module can be seamlessly integrated into existing VLA models. We further propose the L-CALVIN benchmark to systematically evaluate long-horizon manipulation. Extensive experiments on both simulated and real-world tasks demonstrate that Long-VLA significantly outperforms prior state-of-the-art methods, establishing a new baseline for long-horizon robotic control. |
| title | Long-VLA: Unleashing Long-Horizon Capability of Vision Language Action Model for Robot Manipulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2508.19958 |