DriftingVLA: Native One-Step Vision-Language-Action Generation via Per-Dimension Temporal Drifting

2026-08-30

AI Summary

本文提出DriftingVLA模型，通过单次前向传播生成完整动作块，利用每维时间漂移方法解决传统多步细化导致的延迟问题。

Abstract

Conventional flow-based vision-language-action (VLA) models support expressive continuous action generation but rely on multi-step refinement to produce each action chunk, increasing latency in online robot control. To address this issue, we introduce DriftingVLA, a native one-step VLA that generates a complete action chunk with a single action-expert forward pass. Rather than learning a flow field that requires iterative integration at inference, DriftingVLA uses a distribution-drifting objective to learn a direct noise-to-action-chunk mapping for one-step deployment. Since robot action dimensions carry distinct control semantics and distributional characteristics, we further introduce Per-Dimension Temporal Drifting (PDTD). PDTD treats the complete temporal trajectory of each action dimension as a separate drifting unit, enabling finer-grained modeling and shaping of dimension-specific action distributions. This per-dimension decomposition applies only to the training objective; the shared VLA model still generates the complete action chunk jointly, thereby preserving cross-dimensional dependencies. DriftingVLA achieves 98.32% success on LIBERO, 81.09% on RoboTwin 2.0, and 77.67% across six real-world single- and dual-arm tasks, outperforming the evaluated multi-step flow policy and one-step VLA baselines. Native one-step deployment also delivers a 3.36-fold speedup in action-chunk generation, eliminating iterative refinement without sacrificing control performance.

Problem

• Vision-Language-Action
• One-Step Generation
• Temporal Drifting
• Robot Control
• Latency

Innovation

• Native One-Step Generation
• Per-Dimension Temporal Drifting
• Distribution-Drifting Objective