Broadly transferable world model across tasks, modalities, and datasets

Develop a broadly transferable embodied AI world model that generalizes robustly across diverse tasks, sensing modalities, and datasets, achieving reliable performance without relying on domain-specific evaluation protocols or task subsets.

Background

The survey compares world-model-based agents on the DeepMind Control Suite and highlights that differing evaluation setups and task selections hinder fair comparisons of generalization. Within this context, the authors explicitly note that constructing a single model capable of broad transfer across tasks, modalities, and datasets remains unresolved.

This open challenge reflects the need for a unified approach to generalization in embodied AI, where models must handle heterogeneous inputs and objectives while maintaining reliable performance across varied benchmarks and data sources.

References

However, inconsistent evaluation protocols and task subsets impede a fair assessment of generalization, and building a broadly transferable model across tasks, modalities, and datasets remains an open challenge.

— A Comprehensive Survey on World Models for Embodied AI  (2510.16732 - Li et al., 19 Oct 2025) in Section 5.3 (Control Tasks): Evaluation on DMC

Our evaluation is limited to WorldArena and RoboTwin, with Track~2 covering only the Adjust Bottle task, so generalization to other tasks, embodiments, and real robots remains unverified.

— DreamX-Phi 1.0: Action-Conditioned Video World Model for Robotic Manipulation  (2608.13489 - Team et al., 13 Aug 2026) in Section Limitations

Current efficient models are often optimized for specific modality pairs (e.g., vision--language), leaving their transferability to new tasks or missing-modality scenarios unproven.

— From Models to Systems: A Comprehensive Survey of Efficient Multimodal Learning  (2609.19445 - Wang et al., 16 Sep 2026) in Section 10.2, “Multimodal Multi-Task Generalization & Robustness”

Although our simulation findings are further supported by experiments on real-robot data, it remains possible that some effects change with substantially larger models and datasets. Future work should examine these scaling effects by validating the most promising designs under larger training budgets, broader data distributions, and closed-loop real-robot evaluation.

— What Matters in Designing World Action Models: An Empirical Study  (2609.24048 - Tang et al., 21 Sep 2026) in Section Discussion, “Limitations and Future Work”

Strong controls show both what works—low-dimensional motion modeling—and what remains unresolved—perceptual delivery, language dependence, and cross-robot transfer.

— AcrossVAM1.0: Particle World Modeling for Text-Assisted Robot Video Prediction  (2608.28491 - Zhang et al., 28 Aug 2026) in Section 6, Conclusion, p. 8

We train a separate WMM checkpoint on each benchmark's training split; joint training of one WMM across all benchmarks is left to future work.

— World Motion Models: Flexible Sequence Modeling of SE(3) Trajectories  (2610.01742 - Lei et al., 1 Oct 2026) in Section 3, “Experiments,” opening paragraph

Our target-task experiments adapt the video predictor and therefore do not establish zero-shot transfer of the complete video--action model.

— OpenWAM: An Open Framework for Composable World-Action Models  (2610.07922 - Yu et al., 6 Oct 2026) in Discussion and Conclusion, Section 6

While specialised domain-specific approaches exist for particular visual tasks, general-purpose visual reasoning remains an unsolved challenge.

— Can machines reason non-verbally?  (2610.13925 - Saeed et al., 9 Oct 2026) in Section “The gap between verbal and non-verbal reasoning in machines”

We conjecture that this pretrained representation enables robust and consistent world modeling, which relaxes the necessity for task-specific data coverage.

— DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning  (2411.04983 - Zhou et al., 2024) in Section 1, Introduction

Although vision--language learning and predictive video modeling enable machines to recognize objects, describe scenes, interpret activities, and anticipate visual changes~\citep{radford2021clip,li2023blip2,liu2023llava,bai2025qwen25vl,assran2025vjepa2}, building a coherent understanding of the world across viewpoints and time remains an open challenge~\citep{yang2025vsi,wang2026mindcube,lin2025mmsivideo}.

— Holi-World: Building Holistic World Intelligence from Human-Ego Experience  (2610.26549 - Li et al., 19 Oct 2026) in Section 1, Introduction