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The Essential Role of Causality in Foundation World Models for Embodied AI
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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.
Forward citations
Cited by 12 Pith papers
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Autonomous Video Generation with Counterfactual Controllability for Self-Evolving World Models
Video generation provides partial world models; counterfactual controllability is presented as the key requirement for self-evolving ones.
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CARL-GT: Evaluating Causal Reasoning Capabilities of Large Language Models
A causal reasoning benchmark with graph and tabular data shows open-source LLMs are weak at causal discovery from tables, and reveals that task correlations do not align with the benchmark's three categories.
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Exploring the Link Between Bayesian Inference and Embodied Intelligence: Toward Open Physical-World Embodied AI Systems
A position paper arguing that Bayesian inference could become a key design principle for embodied AI in open physical worlds, using Sutton's search-and-learning lens to explain its current absence.
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From 2D to 3D Cognition: A Brief Survey of General World Models
A survey proposing a two-pillar, three-capability framework that organizes recent AI world models by their transition from 2D visual prediction to 3D cognition.
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