dVLA: Diffusion Vision-Language-Action Model with Multimodal Chain-of-Thought
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914067104399360 |
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| author | Wen, Junjie Zhu, Minjie Liu, Jiaming Liu, Zhiyuan Yang, Yicun Zhang, Linfeng Zhang, Shanghang Zhu, Yichen Xu, Yi |
| author_facet | Wen, Junjie Zhu, Minjie Liu, Jiaming Liu, Zhiyuan Yang, Yicun Zhang, Linfeng Zhang, Shanghang Zhu, Yichen Xu, Yi |
| contents | Vision-Language-Action (VLA) models are emerging as a next-generation paradigm for robotics. We introduce dVLA, a diffusion-based VLA that leverages a multimodal chain-of-thought to unify visual perception, language reasoning, and robotic control in a single system. dVLA jointly optimizes perception, language understanding, and action under a single diffusion objective, enabling stronger cross-modal reasoning and better generalization to novel instructions and objects. For practical deployment, we mitigate inference latency by incorporating two acceleration strategies, a prefix attention mask and KV caching, yielding up to around times speedup at test-time inference. We evaluate dVLA in both simulation and the real world: on the LIBERO benchmark, it achieves state-of-the-art performance with a 96.4% average success rate, consistently surpassing both discrete and continuous action policies; on a real Franka robot, it succeeds across a diverse task suite, including a challenging bin-picking task that requires multi-step planning, demonstrating robust real-world performance. Together, these results underscore the promise of unified diffusion frameworks for practical, high-performance VLA robotics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25681 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | dVLA: Diffusion Vision-Language-Action Model with Multimodal Chain-of-Thought Wen, Junjie Zhu, Minjie Liu, Jiaming Liu, Zhiyuan Yang, Yicun Zhang, Linfeng Zhang, Shanghang Zhu, Yichen Xu, Yi Robotics Computer Vision and Pattern Recognition Vision-Language-Action (VLA) models are emerging as a next-generation paradigm for robotics. We introduce dVLA, a diffusion-based VLA that leverages a multimodal chain-of-thought to unify visual perception, language reasoning, and robotic control in a single system. dVLA jointly optimizes perception, language understanding, and action under a single diffusion objective, enabling stronger cross-modal reasoning and better generalization to novel instructions and objects. For practical deployment, we mitigate inference latency by incorporating two acceleration strategies, a prefix attention mask and KV caching, yielding up to around times speedup at test-time inference. We evaluate dVLA in both simulation and the real world: on the LIBERO benchmark, it achieves state-of-the-art performance with a 96.4% average success rate, consistently surpassing both discrete and continuous action policies; on a real Franka robot, it succeeds across a diverse task suite, including a challenging bin-picking task that requires multi-step planning, demonstrating robust real-world performance. Together, these results underscore the promise of unified diffusion frameworks for practical, high-performance VLA robotics. |
| title | dVLA: Diffusion Vision-Language-Action Model with Multimodal Chain-of-Thought |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.25681 |