One of the most promising aspects of WFMs is their ability to learn and generalize across different tasks and domains. Traditional robot policies are often brittle, trained in task-specific environments with carefully curated data and narrow assumptions about sensors, objects, or user behavior. In contrast, WFMs encode broad priors about the physical world, allowing them to interpret unfamiliar sensory inputs, anticipate environmental dynamics, and generate plausible future trajectories. This generalization ability significantly lowers the data and engineering burden associated with deploying robots in new contexts.
Additionally, WFMs can act as rich intermediate representations that bridge perception and control. A WFM could generate structured scene understanding (e.g., object locations, affordances), infer a sequence of actions, and predict intermediate world states. These outputs can then guide low-level control policies or be integrated into planning modules. This modular decomposition—leveraging a WFM as the backbone for reasoning and task inference—allows robotic systems to be more adaptable, interpretable, and robust.
Finally, the scalability of WFMs enables continual improvement and task expansion through techniques like transfer learning, fine-tuning, and reinforcement learning. Robots powered by WFMs could benefit from global-scale learning, where updates based on one robot’s experiences can enhance the capabilities of others. This federated improvement loop—central to the vision of generalist agents—becomes viable when the policy backbone is itself a large, expressive, and data-driven model like a WFM.