# Do World Action Models Generalize Better than VLAs? A Robustness Study

arXiv: https://arxiv.org/abs/2603.22078 · HTML: https://mlsanity.com/paper/2603.22078/

## Key facts

- Cited by 36 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 71 of its 77 references that are in the map.
- First posted on arXiv 23 Mar 2026.

## Placed in

- World models / [Embodied and robot world models](https://mlsanity.com/wm/embodied-and-robot-world-models/)
- Vision-language-action models / [World model-based VLA](https://mlsanity.com/vla/world-model-based-vla/)

## Builds on (most cited first, 30 of 71)

- [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)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022)
- [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025)
- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Octo: An Open-Source Generalist Robot Policy](https://mlsanity.com/paper/2405.12213/) (2024)
- [Learning Latent Dynamics for Planning from Pixels](https://mlsanity.com/paper/1811.04551/) (2018)
- [Mastering Diverse Domains through World Models](https://mlsanity.com/paper/2301.04104/) (2023)
- [Cosmos World Foundation Model Platform for Physical AI](https://mlsanity.com/paper/2501.03575/) (2025)
- [Open X-Embodiment: Robotic Learning Datasets and RT-X Models](https://mlsanity.com/paper/2310.08864/) (2023)
- [V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning](https://mlsanity.com/paper/2506.09985/) (2025)
- [Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models](https://mlsanity.com/paper/1805.12114/) (2018)
- [When to Trust Your Model: Model-Based Policy Optimization](https://mlsanity.com/paper/1906.08253/) (2019)
- [RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation](https://mlsanity.com/paper/2506.18088/) (2025)
- [TD-MPC2: Scalable, Robust World Models for Continuous Control](https://mlsanity.com/paper/2310.16828/) (2023)
- [CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models](https://mlsanity.com/paper/2503.22020/) (2025)
- [UniVLA: Learning to Act Anywhere with Task-centric Latent Actions](https://mlsanity.com/paper/2505.06111/) (2025)
- [Temporal Difference Learning for Model Predictive Control](https://mlsanity.com/paper/2203.04955/) (2022)
- [WorldVLA: Towards Autoregressive Action World Model](https://mlsanity.com/paper/2506.21539/) (2025)
- [Fast-WAM: Do World Action Models Need Test-time Future Imagination?](https://mlsanity.com/paper/2603.16666/) (2026)
- [Causal World Modeling for Robot Control](https://mlsanity.com/paper/2601.21998/) (2026)
- [X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model](https://mlsanity.com/paper/2510.10274/) (2025)
- [LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models](https://mlsanity.com/paper/2510.13626/) (2025)
- [Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning](https://mlsanity.com/paper/2601.16163/) (2026)
- [Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations](https://mlsanity.com/paper/2412.14803/) (2024)
- [Deep Visual Foresight for Planning Robot Motion](https://mlsanity.com/paper/1610.00696/) (2016)
- [Navigation World Models](https://mlsanity.com/paper/2412.03572/) (2024)

## Built on by (most cited first, 30 of 36)

- [Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising](https://mlsanity.com/paper/2604.26694/) (2026)
- [Qwen-RobotManip Technical Report: Alignment Unlocks Scale for Robotic Manipulation Foundation Models](https://mlsanity.com/paper/2606.17846/) (2026)
- [ImageWAM: Do World Action Models Really Need Video Generation, or Just Image Editing?](https://mlsanity.com/paper/2606.19531/) (2026)
- [OA-WAM: Object-Addressable World Action Model for Robust Robot Manipulation](https://mlsanity.com/paper/2605.06481/) (2026)
- [Flash-WAM: Modality-Aware Distillation for World Action Models](https://mlsanity.com/paper/2606.05254/) (2026)
- [Disentangled Robot Learning via Separate Forward and Inverse Dynamics Pretraining](https://mlsanity.com/paper/2604.16391/) (2026)
- [MemoryWAM: Efficient World Action Modeling with Persistent Memory](https://mlsanity.com/paper/2606.20562/) (2026)
- [ST-WAM: Semantic-Temporal World Action Model for Robust Manipulation under Visual Distribution Shifts](https://mlsanity.com/paper/2607.28993/) (2026)
- [Faster-WAM: Do World Action Models Need Deep Action Modules?](https://mlsanity.com/paper/2608.02365/) (2026)
- [World-Action Models for Robot Learning and Control: A Survey](https://mlsanity.com/paper/2609.16074/) (2026)
- [Attacking the Trusted Imagination: Oracle-Level Integrity Attacks on Imagine-then-Act World Models](https://mlsanity.com/paper/2606.22966/) (2026)
- [Steering Robustness into World Action Models via Mechanistic Interpretability and Optimal Control](https://mlsanity.com/paper/2607.14943/) (2026)
- [Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models](https://mlsanity.com/paper/2608.05903/) (2026)
- [Coding Agents with an Obstacle-Aware Harness for Safe Robot Manipulation](https://mlsanity.com/paper/2609.20822/) (2026)
- [Validate the Dream Before You Trust Its Verdict: Admissibility for World-Model Simulators](https://mlsanity.com/paper/2607.07196/) (2026)
- [GWM-VLA: Geometry-Aware Latent World Modeling for Vision-Language-Action Learning](https://mlsanity.com/paper/2608.07619/) (2026)
- [Selective Cross-View Consistency for World Action Models: Held-Out Viewpoint Robustness Without Test-Time Camera Information](https://mlsanity.com/paper/2608.21402/) (2026)
- [Reasoning Without Inference Cost: Latent Semantic Scaffolding for Robot VLA Policies](https://mlsanity.com/paper/2609.04893/) (2026)
- [IndustrialVLA-Bench: A Traceable Multi-Axis Evaluation of Open Robot Policy Models](https://mlsanity.com/paper/2609.25562/) (2026)
- [DeepInsight II: One Trace from Benchmark to Robot](https://mlsanity.com/paper/2608.16556/) (2026)
- [CSWAM: Better Causal Semantic Representations for Out-of-Distribution Generalization in World Action Models](https://mlsanity.com/paper/2609.18462/) (2026)
- [The Right Future for Action: Learning Action-Relevant Predictive States in World Action Models](https://mlsanity.com/paper/2609.23369/) (2026)
- [What Matters in Designing World Action Models: An Empirical Study](https://mlsanity.com/paper/2609.24048/) (2026)
- [Think Like a World Model, Act Like a VLA: Distilling World-Model Representations into Compact Robot Policies](https://mlsanity.com/paper/2609.24682/) (2026)
- [Copper-Policy: Focus on the Representation for Robust Robot Manipulation](https://mlsanity.com/paper/2609.32779/) (2026)
- [DeltaWAM: Change-Centric Visual Foresight via Delta Tokens for an Efficient World-Action Model](https://mlsanity.com/paper/2609.33177/) (2026)
- [Learning to Act under Visual Interruptions with Vision-Language-Action Models](https://mlsanity.com/paper/2609.35003/) (2026)
- [Staircase Policy: Streaming Inference for World-Action Models with Large Action Chunks](https://mlsanity.com/paper/2609.36471/) (2026)
- [RawVLA: Embodied Neural Image Signal Processor For Robotic Manipulation](https://mlsanity.com/paper/2609.37530/) (2026)
- [MVG-WAM: Multiple View Geometry-Aware World-Action Modeling for Robotic Manipulation](https://mlsanity.com/paper/2609.37793/) (2026)

## Abstract (arXiv)

Robot action planning in the real world is challenging as it requires not only understanding the current state of the environment but also predicting how it will evolve in response to actions. Vision-language-action (VLA), which repurpose large-scale vision-language models for robot action generation using action experts, have achieved notable success across a variety of robotic tasks. Nevertheless, their performance remains constrained by the scope of their training data, exhibiting limited generalization to unseen scenarios and vulnerability to diverse contextual perturbations. More recently, world models have been revisited as an alternative to VLAs. These models, referred to as world action models (WAMs), are built upon world models that are trained on large corpora of video data to predict future states. With minor adaptations, their latent representation can be decoded into robot actions. It has been suggested that their explicit dynamic prediction capacity, combined with spatiotemporal priors acquired from web-scale video pretraining, enables WAMs to generalize more effectively than VLAs. In this paper, we conduct a comparative study of prominent state-of-the-art VLA policies and recently released WAMs. We evaluate their performance on the LIBERO-Plus and RoboTwin 2.0-Plus benchmarks under various visual and language perturbations. Our results show that WAMs achieve strong robustness, with LingBot-VA reaching 74.2% success rate on RoboTwin 2.0-Plus and Cosmos-Policy achieving 82.2% on LIBERO-Plus. While VLAs such as π0.5 can achieve comparable robustness on certain tasks, they typically require extensive training with diverse robotic datasets and varied learning objectives. The evaluation code for the RoboTwin2.0-Plus benchmark is available at: https://robot-robustness.github.io/RoboTwin2.0-Plus/.