# Goal-VLA: Image-Generative VLMs as Object-Centric World Models Empowering Zero-shot Robot Manipulation

arXiv: https://arxiv.org/abs/2506.23919 · HTML: https://mlsanity.com/paper/2506.23919/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and robot world models, one of 11 directions in World models.
- Builds on 15 of its 50 references that are in the map.
- First posted on arXiv 30 Jun 2025.

## Placed in

- World models / [Embodied and robot world models](https://mlsanity.com/wm/embodied-and-robot-world-models/)

## Builds on (most cited first, 15 of 15)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://mlsanity.com/paper/2401.14159/) (2024)
- [RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation](https://mlsanity.com/paper/2410.07864/) (2024)
- [DayDreamer: World Models for Physical Robot Learning](https://mlsanity.com/paper/2206.14176/) (2022)
- [3D-VLA: A 3D Vision-Language-Action Generative World Model](https://mlsanity.com/paper/2403.09631/) (2024)
- [ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation](https://mlsanity.com/paper/2409.01652/) (2024)
- [A Survey on Vision-Language-Action Models for Embodied AI](https://mlsanity.com/paper/2405.14093/) (2024)
- [Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models](https://mlsanity.com/paper/2310.10639/) (2023)
- [Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets](https://mlsanity.com/paper/2504.02792/) (2025)
- [iVideoGPT: Interactive VideoGPTs are Scalable World Models](https://mlsanity.com/paper/2405.15223/) (2024)
- [A Self-Correcting Vision-Language-Action Model for Fast and Slow System Manipulation](https://mlsanity.com/paper/2405.17418/) (2024)

## Built on by (most cited first, 15 of 15)

- [OA-WAM: Object-Addressable World Action Model for Robust Robot Manipulation](https://mlsanity.com/paper/2605.06481/) (2026)
- [RHO: Your Coding Agent is Secretly a Roboticist](https://mlsanity.com/paper/2606.16458/) (2026)
- [Show-Harness: Just a VLM Agent Can Play Robots](https://mlsanity.com/paper/2609.10522/) (2026)
- [LiLa-WAM: Lightweight Latent Reasoning World-Action Model for Robotic Manipulation](https://mlsanity.com/paper/2608.03701/) (2026)
- [H-WM: Robotic Task and Motion Planning Guided by Hierarchical World Model](https://mlsanity.com/paper/2602.11291/) (2026)
- [MultihopSpatial: Multi-hop Compositional Spatial Reasoning Benchmark for Vision-Language Model](https://mlsanity.com/paper/2603.18892/) (2026)
- [AC-VLA: Robust Out-of-Distribution Action Execution via Compositional Learning](https://mlsanity.com/paper/2607.15714/) (2026)
- [Continuous Reasoning for Vision-Language-Action](https://mlsanity.com/paper/2606.00229/) (2026)
- [Pelican-VLA 0.5: Attending Before Acting Benefits Generalization](https://mlsanity.com/paper/2607.06655/) (2026)
- [SWEET: Sparse World Modeling with Image Editing for Embodied Task Execution](https://mlsanity.com/paper/2605.19319/) (2026)
- [GeoAlign: Beyond Semantics with State-Guided Spatial Alignment in VLA Models](https://mlsanity.com/paper/2606.03240/) (2026)
- [World Models for Embodied Intelligence: From Plausible to Controllable to Actionable](https://mlsanity.com/paper/2609.16697/) (2026)
- [LAMP: Lift Image-Editing as General 3D Priors for Open-world Manipulation](https://mlsanity.com/paper/2604.08475/) (2026)
- [GIFT: Goal-Injected Fine-Tuning for Efficient Manipulation Policy Adaptation](https://mlsanity.com/paper/2609.07006/) (2026)
- [Dynin-Robotics: Omnimodal Unified Diffusion Vision-Language-Action Model](https://mlsanity.com/paper/2609.13053/) (2026)

## Abstract (arXiv)

Generalization remains a fundamental challenge in robotic manipulation. To tackle this challenge, recent Vision-Language-Action (VLA) models build policies on top of Vision-Language Models (VLMs), seeking to transfer their open-world semantic knowledge. However, their zero-shot capability lags significantly behind the base VLMs, as the instruction-vision-action data is too limited to cover diverse scenarios, tasks, and robot embodiments. In this work, we present Goal-VLA, a zero-shot framework that leverages Image-Generative VLMs as world models to generate desired goal states, from which the target object pose is derived to enable generalizable manipulation. The key insight is that object state representation is the golden interface, naturally separating a manipulation system into high-level and low-level policies. This representation abstracts away explicit action annotations, allowing the use of highly generalizable VLMs while simultaneously providing spatial cues for training-free low-level control. To further improve robustness, we introduce a Reflection-through-Synthesis process that iteratively validates and refines the generated goal image before execution. Both simulated and real-world experiments demonstrate that our name achieves strong performance and inspiring generalizability in manipulation tasks. Supplementary materials are available at https://nus-lins-lab.github.io/goalvlaweb/.