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

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