The Essential Role of Causality in Foundation World Models for Embodied AI
This addresses the problem of enabling generally capable embodied agents to perform new tasks in real-world environments, but it is incremental as it focuses on a viewpoint rather than a new method or result.
The paper argues that current foundation models are insufficient for Embodied AI due to inaccurate modeling of physical interactions, and posits that integrating causality is vital for building veridical world models to enable meaningful interactions.
Recent advances in foundation models, especially in large multi-modal models and conversational agents, have ignited interest in the potential of generally capable embodied agents. Such agents will require the ability to perform new tasks in many different real-world environments. However, current foundation models fail to accurately model physical interactions and are therefore insufficient for Embodied AI. The study of causality lends itself to the construction of veridical world models, which are crucial for accurately predicting the outcomes of possible interactions. This paper focuses on the prospects of building foundation world models for the upcoming generation of embodied agents and presents a novel viewpoint on the significance of causality within these. We posit that integrating causal considerations is vital to facilitating meaningful physical interactions with the world. Finally, we demystify misconceptions about causality in this context and present our outlook for future research.