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This research addresses a critical bottleneck in Vision-Language-Action (VLA) models by replacing direct geometric feature alignment with relational spatial alignment to prevent rank collapse. The proposed method successfully injects 3D spatial awareness into robotic reasoning without sacrificing the model's original expressivity, as validated across public benchmarks and real-world manipulation tasks.
Relational Spatial Alignment for Vision-Language-Action Models
arXiv:2610.24705v1 Announce Type: new Abstract: Spatial understanding is essential for robots to perceive their surrounding environments and execute actions. Existing approaches typically encode 3D awareness into vision-language-action (VLA) models by directly aligning their representations with pre-trained 3D foundation models. However, we observe that such direct geometric feature alignment essentially exploits 3D foundation priors as a form of low-rank regularization in the feature space, resulting in severe rank collapse of the pre-trained VLA representations. In contrast, we shift the focus from enforcing point-wise equality to aligning pair-wise relationships. We propose a representation-alignment method that enables the injection of 3D spatial awareness without compromising the model's original expressivity. Extensive experiments on public benchmarks, our new spatial reasoning benchmark, and real-world manipulation scenarios demonstrate that our approach effectively incorporates 3D spatial representations into VLA reasoning, achieving robust performance and enhanced spatial awareness across diverse manipulation tasks.
Abstract:Spatial understanding is essential for robots to perceive their surrounding environments and execute actions. Existing approaches typically encode 3D awareness into vision-language-action (VLA) models by directly aligning their representations with pre-trained 3D foundation models. However, we observe that such direct geometric feature alignment essentially exploits 3D foundation priors as a form of low-rank regularization in the feature space, resulting in severe rank collapse of the pre-trained VLA representations. In contrast, we shift the focus from enforcing point-wise equality to aligning pair-wise relationships. We propose a representation-alignment method that enables the injection of 3D spatial awareness without compromising the model's original expressivity. Extensive experiments on public benchmarks, our new spatial reasoning benchmark, and real-world manipulation scenarios demonstrate that our approach effectively incorporates 3D spatial representations into VLA reasoning, achieving robust performance and enhanced spatial awareness across diverse manipulation tasks.
Robotics (cs.RO)
arXiv:2610.24705 [cs.RO]
(or arXiv:2610.24705v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.24705 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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