$\Delta$VLA: Prior-Guided Vision-Language-Action Models via World Knowledge Variation

TL;DR

The paper introduces $ riangle$VLA, a novel framework for vision-language-action in robotics that models world knowledge variations relative to a prior, enhancing reasoning and efficiency in action generation.

Contribution

It proposes a prior-guided approach with new modules for extracting, encoding, and disentangling world knowledge variations, improving over existing forecasting methods.

Findings

Achieves state-of-the-art results on robotic manipulation benchmarks.

Demonstrates improved efficiency and reasoning in real-world tasks.

Outperforms prior models in accuracy and robustness.

Abstract

Recent vision-language-action (VLA) models have significantly advanced robotic manipulation by unifying perception, reasoning, and control. To achieve such integration, recent studies adopt a predictive paradigm that models future visual states or world knowledge to guide action generation. However, these models emphasize forecasting outcomes rather than reasoning about the underlying process of change, which is essential for determining how to act. To address this, we propose $\Delta$VLA, a prior-guided framework that models world-knowledge variations relative to an explicit current-world knowledge prior for action generation, rather than regressing absolute future world states. Specifically, 1) to construct the current world knowledge prior, we propose the Prior-Guided WorldKnowledge Extractor (PWKE). It extracts manipulable regions, spatial relations, and semantic cues from the visual input, guided by auxiliary heads and prior pseudo labels, thus reducing redundancy. 2) Building upon this, to represent how world knowledge evolves under actions, we introduce the Latent World Variation Quantization (LWVQ). It learns a discrete latent space via a VQ-VAE objective to encode world knowledge variations, shifting prediction from full modalities to compact latent. 3)Moreover, to mitigate interference during variation modeling, we design the Conditional Variation Attention (CV-Atten), whichpromotes disentangled learning and preserves the independence of knowledge representations. Extensive experiments on both simulated benchmarks and real-world robotic tasks demonstrate $\Delta$VLA achieves state-of-the-art performance while improving efficiency. Code and real-world execution videos are available at https://github.com/JiuTian-VL/DeltaVLA.