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The Essential Role of Causality in Foundation World Models for Embodied AI

Tarun Gupta, Wenbo Gong, Chao Ma, Nick Pawlowski, Agrin Hilmkil, Meyer Scetbon, Marc Rigter, Ade Famoti, Ashley Juan Llorens, Jianfeng Gao, Stefan Bauer, Danica Kragic, Bernhard Schölkopf, Cheng Zhang

TL;DR

This paper argues that current foundation models fall short for Embodied AI because they lack veridical, causality-aware world representations. It introduces Foundation Veridical World Models (FVWM) as multi-modal, causally grounded systems capable of representing, predicting, and counterfactually reasoning about physical interactions to enable generalization across environments. The authors critique canonical SEM/PO approaches as limited for real-world, multi-modal data and outline misconceptions in causal ML, advocating for empirically-driven, data-rich development that leverages online and offline interactions. They propose concrete research directions, including diverse modalities, online/offline interaction paradigms, latent dynamic representations, and scalable evaluation, to advance planning, safety, and deployment of embodied agents. The work emphasizes empirical benchmarks and cross-disciplinary integration to realize robust, scalable Embodied AI with causal foundations, alongside practical considerations for general and specialized robot deployment.

Abstract

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.

The Essential Role of Causality in Foundation World Models for Embodied AI

TL;DR

This paper argues that current foundation models fall short for Embodied AI because they lack veridical, causality-aware world representations. It introduces Foundation Veridical World Models (FVWM) as multi-modal, causally grounded systems capable of representing, predicting, and counterfactually reasoning about physical interactions to enable generalization across environments. The authors critique canonical SEM/PO approaches as limited for real-world, multi-modal data and outline misconceptions in causal ML, advocating for empirically-driven, data-rich development that leverages online and offline interactions. They propose concrete research directions, including diverse modalities, online/offline interaction paradigms, latent dynamic representations, and scalable evaluation, to advance planning, safety, and deployment of embodied agents. The work emphasizes empirical benchmarks and cross-disciplinary integration to realize robust, scalable Embodied AI with causal foundations, alongside practical considerations for general and specialized robot deployment.

Abstract

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.
Paper Structure (31 sections, 1 equation, 2 figures)

This paper contains 31 sections, 1 equation, 2 figures.

Table of Contents

  1. Introduction
  2. Embodied AI and Foundation Veridical World Models
  3. The readiness of Embodied AI research
  4. Hardware advancement
  5. Paradigm shift in AI
  6. Data availability
  7. Towards Foundation Veridical World Models
  8. Causality in Foundation World Models
  9. Canonical approaches to causal modelling
  10. Structural Equation Models (SEM)
  11. Theory-oriented research paradigm
  12. Limitations
  13. Demystifying causal modeling for
  14. Misconception I: Causal ML is a separate field from Embodied AI and .
  15. Misconception II: Causal ML research must be theory-oriented.
  16. ...and 16 more sections

Figures (2)

  • Figure 1: The Foundation Veridical World Model (FVWM) is designed to comprehend associations, counterfactuals, and interactions within the world, aiding any embodied AI agent in executing tasks across diverse environments and platforms.
  • Figure 2: A veridical world model can predict the consequences of a sequence of actions and corresponding counterfactuals correctly with confidence, ignoring irrelevant information like color of the cup. For example, the model should correctly predict that the cup will break if 'open drawer' action is not taken in the sequence. Images taken from AI2THOR kolve2017ai2 simulator.