PD-VLA: Accelerating Vision-Language-Action Model Integrated with Action Chunking via Parallel Decoding
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908851378323456 |
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| author | Song, Wenxuan Chen, Jiayi Ding, Pengxiang Zhao, Han Zhao, Wei Zhong, Zhide Ge, Zongyuan Li, Zhijun Wang, Donglin Ma, Jun Wang, Lujia Li, Haoang |
| author_facet | Song, Wenxuan Chen, Jiayi Ding, Pengxiang Zhao, Han Zhao, Wei Zhong, Zhide Ge, Zongyuan Li, Zhijun Wang, Donglin Ma, Jun Wang, Lujia Li, Haoang |
| contents | Vision-Language-Action (VLA) models demonstrate remarkable potential for generalizable robotic manipulation. The performance of VLA models can be improved by integrating with action chunking, a critical technique for effective control. However, action chunking linearly scales up action dimensions in VLA models with increased chunking sizes. This reduces the inference efficiency. To tackle this problem, we propose PD-VLA, the first parallel decoding framework for VLA models integrated with action chunking. Our framework reformulates autoregressive decoding as a nonlinear system solved by parallel fixed-point iterations. This approach preserves model performance with mathematical guarantees while significantly improving decoding speed. In addition, it enables training-free acceleration without architectural changes, as well as seamless synergy with existing acceleration techniques. Extensive simulations validate that our PD-VLA maintains competitive success rates while achieving 2.52 times execution frequency on manipulators (with 7 degrees of freedom) compared with the fundamental VLA model. Furthermore, we experimentally identify the most effective settings for acceleration. Finally, real-world experiments validate its high applicability across different tasks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_02310 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PD-VLA: Accelerating Vision-Language-Action Model Integrated with Action Chunking via Parallel Decoding Song, Wenxuan Chen, Jiayi Ding, Pengxiang Zhao, Han Zhao, Wei Zhong, Zhide Ge, Zongyuan Li, Zhijun Wang, Donglin Ma, Jun Wang, Lujia Li, Haoang Robotics Computer Vision and Pattern Recognition Vision-Language-Action (VLA) models demonstrate remarkable potential for generalizable robotic manipulation. The performance of VLA models can be improved by integrating with action chunking, a critical technique for effective control. However, action chunking linearly scales up action dimensions in VLA models with increased chunking sizes. This reduces the inference efficiency. To tackle this problem, we propose PD-VLA, the first parallel decoding framework for VLA models integrated with action chunking. Our framework reformulates autoregressive decoding as a nonlinear system solved by parallel fixed-point iterations. This approach preserves model performance with mathematical guarantees while significantly improving decoding speed. In addition, it enables training-free acceleration without architectural changes, as well as seamless synergy with existing acceleration techniques. Extensive simulations validate that our PD-VLA maintains competitive success rates while achieving 2.52 times execution frequency on manipulators (with 7 degrees of freedom) compared with the fundamental VLA model. Furthermore, we experimentally identify the most effective settings for acceleration. Finally, real-world experiments validate its high applicability across different tasks. |
| title | PD-VLA: Accelerating Vision-Language-Action Model Integrated with Action Chunking via Parallel Decoding |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.02310 |