3D-Mix for VLA: A Plug-and-Play Module for Integrating VGGT-based 3D Information into Vision-Language-Action Models

TL;DR

This paper introduces 3D-Mix, a versatile plug-and-play module that systematically integrates 3D spatial information into vision-language-action models, significantly improving robotic manipulation performance across various architectures and benchmarks.

Contribution

It presents a comprehensive study of VGGT integration strategies and proposes 3D-Mix, the first adaptable module that enhances spatial understanding in VLA models without altering existing components.

Findings

Semantic-conditioned gated fusion outperforms other schemes.

3D-Mix improves performance by an average of 7% on OOD benchmarks.

Consistent gains across multiple VLA architectures and models.

Abstract

Vision-Language-Action (VLA) models leverage Multimodal Large Language Models (MLLMs) for robotic control, but recent studies reveal that MLLMs exhibit limited spatial intelligence due to training predominantly on 2D data, resulting in inadequate 3D perception for manipulation tasks. While recent approaches incorporate specialized 3D vision models such as VGGT to enhance spatial understanding, they employ diverse integration mechanisms without systematic investigation, leaving the optimal fusion strategy unclear. We conduct a comprehensive pilot study comparing nine VGGT integration schemes on standardized benchmarks and find that semantic-conditioned gated fusion, which adaptively balances 2D semantic and 3D geometric features based on task context, achieved the strongest performance among all nine evaluated fusion schemes in our pilot study. We present 3D-Mix, a plug-and-play module that integrates into diverse VLA architectures (GR00T-style and $\pi$-style) without modifying existing MLLM or action expert components. Experiments across six MLLM series (nine model variants, 2B--8B parameters) on SIMPLER and LIBERO show that 3D-Mix delivers consistent performance gains, averaging +7.0% on the out-of-domain (OOD) SIMPLER benchmark across all nine GR00T-style variants, establishing a principled approach for enhancing spatial intelligence in VLA systems.