Accelerating Vision-Language-Action Model Integrated with Action Chunking via Parallel Decoding
This work addresses efficiency bottlenecks in robotic manipulation models, offering a training-free acceleration method that is incremental but practically impactful.
The paper tackles the problem of reduced inference efficiency in Vision-Language-Action models when integrated with action chunking, proposing PD-VLA, a parallel decoding framework that maintains competitive success rates while achieving 2.52 times execution frequency on manipulators.
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.