# GeoBridge-VLA: Geometry-Aware Residual Adaptation for Vision-Language-Action Models

arXiv: https://arxiv.org/abs/2610.05026 · HTML: https://mlsanity.com/paper/2610.05026/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 29 of its 35 references that are in the map.
- First posted on arXiv 4 Oct 2026.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)
- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)
- Vision-language-action models / [3D and spatial-aware VLA](https://mlsanity.com/vla/3d-and-spatial-aware-vla/)

## Builds on (most cited first, 29 of 29)

- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning](https://mlsanity.com/paper/2306.03310/) (2023)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success](https://mlsanity.com/paper/2502.19645/) (2025)
- [Depth Anything 3: Recovering the Visual Space from Any Views](https://mlsanity.com/paper/2511.10647/) (2025)
- [ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth](https://mlsanity.com/paper/2302.12288/) (2023)
- [SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics](https://mlsanity.com/paper/2506.01844/) (2025)
- [SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model](https://mlsanity.com/paper/2501.15830/) (2025)
- [TinyVLA: Towards Fast, Data-Efficient Vision-Language-Action Models for Robotic Manipulation](https://mlsanity.com/paper/2409.12514/) (2024)
- [3D-VLA: A 3D Vision-Language-Action Generative World Model](https://mlsanity.com/paper/2403.09631/) (2024)
- [PointVLA: Injecting the 3D World into Vision-Language-Action Models](https://mlsanity.com/paper/2503.07511/) (2025)
- [Can Language Understand Depth?](https://mlsanity.com/paper/2207.01077/) (2022)
- [DepthVLA: Enhancing Vision-Language-Action Models with Depth-Aware Spatial Reasoning](https://mlsanity.com/paper/2510.13375/) (2025)
- [FP3: A 3D Foundation Policy for Robotic Manipulation](https://mlsanity.com/paper/2503.08950/) (2025)
- [3D CAVLA: Leveraging Depth and 3D Context to Generalize Vision Language Action Models for Unseen Tasks](https://mlsanity.com/paper/2505.05800/) (2025)
- [From Spatial to Actions: Grounding Vision-Language-Action Model in Spatial Foundation Priors](https://mlsanity.com/paper/2510.17439/) (2025)
- [QDepth-VLA: Quantized Depth Prediction as Auxiliary Supervision for Vision-Language-Action Models](https://mlsanity.com/paper/2510.14836/) (2025)
- [Evo-Depth: A Lightweight Depth-Enhanced Vision-Language-Action Model](https://mlsanity.com/paper/2605.14950/) (2026)
- [GP3: A 3D Geometry-Aware Policy with Multi-View Images for Robotic Manipulation](https://mlsanity.com/paper/2509.15733/) (2025)
- [RetoVLA: Reusing Register Tokens for Spatial Reasoning in Vision-Language-Action Models](https://mlsanity.com/paper/2509.21243/) (2025)
- [AugVLA-3D: Depth-Driven Feature Augmentation for Vision-Language-Action Models](https://mlsanity.com/paper/2602.10698/) (2026)
- [GIFT: Guided Intermediate Feature Training via Action-Oriented Structural Supervision for Robotic Manipulation](https://mlsanity.com/paper/2609.04193/) (2026)
- [IVRA: Improving Visual-Token Relations for Robot Action Policy with Training-Free Hint-Based Guidance](https://mlsanity.com/paper/2601.16207/) (2026)
- [SPACE-CLIP: Spatial Perception via Adaptive CLIP Embeddings for Monocular Depth Estimation](https://mlsanity.com/paper/2601.17657/) (2026)
- [GaussVLA: Geometry-Aware Spatial Reasoning for Vision-Language-Action Model](https://mlsanity.com/paper/2608.24959/) (2026)

## Abstract (arXiv)

Vision-language-action (VLA) models encode semantic information from vision-language pretraining, but manipulation also requires precise spatial reasoning. We present GeoBridge-VLA, a two-stage method for learning geometric features from a pretrained VLA's frozen visual encoder and using them for action prediction. Stage I trains a feature bridge and geometry decoder with depth supervision. Stage II freezes these modules and trains a gated residual interface together with the action-side projections and action expert. The residual augments the existing visual tokens without adding a second image encoder or increasing the token count. Deployment requires RGB, robot state, and language, but no depth observations. Under matched evaluation conditions, GeoBridge-VLA achieves 70.9% success on LIBERO, compared with 60.0% for SmolVLA. Disabling the residual in the same trained checkpoint reduces success from 70.90% to 69.85%, with mixed effects across suites. On a physical ROBOTIS OMY robot, GeoBridge-VLA succeeds in 148 of 200 trials (74.0%) across four tasks, compared with 108 of 200 (54.0%) for SmolVLA.