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.