From Spatial to Actions: Grounding Vision-Language-Action Model in Spatial Foundation Priors

• URL: http://arxiv.org/abs/2510.17439v1
• Date: Mon, 20 Oct 2025 11:26:45 GMT
• Title: From Spatial to Actions: Grounding Vision-Language-Action Model in Spatial Foundation Priors
• Abstract summary: Existing vision-language-action (VLA) models act in 3D real-world but are typically built on 2D encoders.<n>In this work, we introduce FALCON, a novel paradigm that injects rich 3D spatial tokens into the action head.
• Score: 54.84863164684646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing vision-language-action (VLA) models act in 3D real-world but are typically built on 2D encoders, leaving a spatial reasoning gap that limits generalization and adaptability. Recent 3D integration techniques for VLAs either require specialized sensors and transfer poorly across modalities, or inject weak cues that lack geometry and degrade vision-language alignment. In this work, we introduce FALCON (From Spatial to Action), a novel paradigm that injects rich 3D spatial tokens into the action head. FALCON leverages spatial foundation models to deliver strong geometric priors from RGB alone, and includes an Embodied Spatial Model that can optionally fuse depth, or pose for higher fidelity when available, without retraining or architectural changes. To preserve language reasoning, spatial tokens are consumed by a Spatial-Enhanced Action Head rather than being concatenated into the vision-language backbone. These designs enable FALCON to address limitations in spatial representation, modality transferability, and alignment. In comprehensive evaluations across three simulation benchmarks and eleven real-world tasks, our proposed FALCON achieves state-of-the-art performance, consistently surpasses competitive baselines, and remains robust under clutter, spatial-prompt conditioning, and variations in object scale and height.

Related papers

• 3DThinkVLA: Endowing Vision-Language-Action Models with Latent 3D Priors via 3D-Thinking-Guided Co-training [19.5808550016589]
  3D geometry perception and 3D spatial reasoning are distinct capabilities that can be disentangled and injected at different feature hierarchies.<n>We propose a 3D-thinking-guided co-training framework that enables vision-language-action (VLA) models to perform 3D spatial reasoning implicitly during action prediction.
  arXiv  Detail & Related papers  (2026-06-03T04:34:07Z)
• PointACT: Vision-Language-Action Models with Multi-Scale Point-Action Interaction [57.63073414949329]
  We propose PointACT, a dual-system 3D-aware VLA policy that integrates hierarchical 3D point cloud representations directly into the action decoding process.<n>PointACT employs a multi-scale point-action interaction mechanism with efficient bottleneck window self-attention, enabling evolving action tokens to densely attend to both local geometric detail and global scene structure.
  arXiv  Detail & Related papers  (2026-05-20T17:10:31Z)
• VEGA: Visual Encoder Grounding Alignment for Spatially-Aware Vision-Language-Action Models [55.12929235609365]
  Visual backbones of current vision-language-action (VLA) models are predominantly pretrained on 2D image data without explicit 3D geometric supervision.<n>Existing implicit grounding methods partially address this by aligning VLA features with those of 3D-aware foundation models.<n>We propose VEGA, a framework that directly aligns the output of the VLA's visual encoder with spatially-aware features from DINOv2-FiT3D.
  arXiv  Detail & Related papers  (2026-05-11T12:44:26Z)
• XEmbodied: A Foundation Model with Enhanced Geometric and Physical Cues for Large-Scale Embodied Environments [26.90783926543698]
  Cloud pipelines rely on generic vision-language models (VLMs) that lack geometric reasoning and domain semantics.<n>We propose XEmbodied, a cloud-side foundation model that endowsVLMs with intrinsic 3D geometric awareness.<n> XEmbodied preserves general capabilities while demonstrating robust performance across 18 public benchmarks.
  arXiv  Detail & Related papers  (2026-04-20T16:37:16Z)
• LoST: Level of Semantics Tokenization for 3D Shapes [50.847769883816085]
  State-of-the-art methods rely on geometric level-of-detail (LoD) hierarchies, originally designed for rendering and compression.<n>We propose Level-of-Semantics Tokenization (LoST), which orders salience, such that early prefixes decode complete, plausible shapes.<n>LoST achieves SOTA reconstruction, surpassing previous LoD-based 3D shape tokenizers by large margins on both geometric and semantic reconstruction metrics.
  arXiv  Detail & Related papers  (2026-03-18T17:56:06Z)
• SoPE: Spherical Coordinate-Based Positional Embedding for Enhancing Spatial Perception of 3D LVLMs [21.891285551179365]
  We introduce Spherical Coordinate-based Positional Embedding (SoPE)<n>Our method maps point-cloud token indices into a 3D spherical coordinate space, enabling unified modeling of spatial locations and directional angles.<n>This formulation preserves the inherent geometric structure of point-cloud data, enhances spatial awareness, and yields more consistent and expressive geometric representations for multimodal learning.
  arXiv  Detail & Related papers  (2026-02-26T07:42:15Z)
• Spa3R: Predictive Spatial Field Modeling for 3D Visual Reasoning [43.746951848993035]
  spatial intelligence can emerge from 2D vision alone, rather than being imposed via explicit spatial instruction tuning.<n>We introduce Spa3R, a self-supervised framework that learns a unified view-in spatial representation directly from unposed multi-view images.<n>Experiments demonstrate Spa3-VLM achieves state-of-the-art accuracy of 58.6% on 3D VQA, significantly outperforming prior methods.
  arXiv  Detail & Related papers  (2026-02-24T18:37:34Z)
• Lemon: A Unified and Scalable 3D Multimodal Model for Universal Spatial Understanding [80.66591664266744]
  Lemon is a unified transformer architecture that processes 3D point cloud patches and language tokens as a single sequence.<n>To handle the complexity of 3D data, we develop a structured patchification and tokenization scheme that preserves spatial context.<n>Lemon establishes new state-of-the-art performance across comprehensive 3D understanding and reasoning tasks.
  arXiv  Detail & Related papers  (2025-12-14T20:02:43Z)
• ReSem3D: Refinable 3D Spatial Constraints via Fine-Grained Semantic Grounding for Generalizable Robotic Manipulation [12.059517583878756]
  We propose ReSem3D, a unified manipulation framework for semantically diverse environments.<n>We show that ReSem3D performs diverse manipulation tasks under zero-shot conditions, exhibiting strong adaptability and generalization.
  arXiv  Detail & Related papers  (2025-07-24T10:07:31Z)
• Evo-0: Vision-Language-Action Model with Implicit Spatial Understanding [11.222744122842023]
  We introduce a plug-and-play module that implicitly incorporates 3D geometry features into Vision-Language-Action (VLA) models.<n>Our method significantly improves the performance of state-of-the-art VLA models across diverse scenarios.
  arXiv  Detail & Related papers  (2025-07-01T04:05:47Z)
• Agentic 3D Scene Generation with Spatially Contextualized VLMs [67.31920821192323]
  We introduce a new paradigm that enables vision-language models to generate, understand, and edit complex 3D environments.<n>We develop an agentic 3D scene generation pipeline in which the VLM iteratively reads from and updates the spatial context.<n>Results show that our framework can handle diverse and challenging inputs, achieving a level of generalization not observed in prior work.
  arXiv  Detail & Related papers  (2025-05-26T15:28:17Z)
• MetaSpatial: Reinforcing 3D Spatial Reasoning in VLMs for the Metaverse [8.249725689628923]
  We present MetaSpatial, the first reinforcement learning-based framework designed to enhance 3D spatial reasoning in vision-language models (VLMs)<n>Our key innovation is a multi-turn RL-based optimization mechanism that integrates physics-aware constraints and rendered image evaluations, ensuring generated 3D layouts are coherent, physically plausible, and aesthetically consistent.
  arXiv  Detail & Related papers  (2025-03-24T09:18:01Z)
• Cross-Modal and Uncertainty-Aware Agglomeration for Open-Vocabulary 3D Scene Understanding [58.38294408121273]
  We propose Cross-modal and Uncertainty-aware Agglomeration for Open-vocabulary 3D Scene Understanding dubbed CUA-O3D.<n>Our method addresses two key challenges: (1) incorporating semantic priors from VLMs alongside the geometric knowledge of spatially-aware vision foundation models, and (2) using a novel deterministic uncertainty estimation to capture model-specific uncertainties.
  arXiv  Detail & Related papers  (2025-03-20T20:58:48Z)
• GEAL: Generalizable 3D Affordance Learning with Cross-Modal Consistency [50.11520458252128]
  Existing 3D affordance learning methods struggle with generalization and robustness due to limited annotated data.<n>We propose GEAL, a novel framework designed to enhance the generalization and robustness of 3D affordance learning by leveraging large-scale pre-trained 2D models.<n>GEAL consistently outperforms existing methods across seen and novel object categories, as well as corrupted data.
  arXiv  Detail & Related papers  (2024-12-12T17:59:03Z)
• REVISION: Rendering Tools Enable Spatial Fidelity in Vision-Language Models [67.55362046790512]
  Vision-language models lack the ability to correctly reason over spatial relationships. We develop the REVISION framework which improves spatial fidelity in vision-language models. Our results and findings indicate that utilizing rendering-based frameworks is an effective approach for developing spatially-aware models.
  arXiv  Detail & Related papers  (2024-08-05T04:51:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.