Grounded world models in biological organisms and future embodied AI

• URL: http://arxiv.org/abs/2607.13560v1
• Date: Wed, 15 Jul 2026 08:02:19 GMT
• Title: Grounded world models in biological organisms and future embodied AI
• Abstract summary: Recent advances in generative and embodied AI have been driven by large-scale predictive learning over multimodal data.<n>Neuroscience and cognitive science suggest that biological intelligence is organized in the opposite way.
• Score: 0.3015442485490763
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in generative and embodied AI have been driven by large-scale predictive learning over multimodal data. However, the resulting systems remain largely based on passive training regimes where linguistic regularities create the scaffold onto which information from other modalities is attached. Conversely, neuroscience and cognitive science suggest that biological intelligence is organized in the opposite way, where grounded world models acquired through interaction with the environment provide the semantic scaffold to which language is attached. Here, we illustrate five examples of neural circuits supporting grounded world modelling, which underlie navigation in physical and conceptual spaces, affordance-based perception and interaction with objects, active perception and exploratory learning, allostatic control and emotion, and the distinction between self- and world-generated outcomes. These examples highlight several features largely missing from current embodied AI, including the role of intrinsic dynamics as a foundation for learning, the centrality of action in aligning these dynamics with the external world, the prominence of autonomous experience and open-ended learning over passive assimilation of externally provided data, and the fact that early predictive and control mechanisms scaffold higher cognitive abilities such as reasoning, conceptual navigation, planning, imagination, understanding others' minds, and communication. Finally, we discuss whether and how principles derived from biological systems may inform future embodied AI, including training regimes based on social interaction to construct world models that are not only grounded but also socially shared and aligned with human norms and values.

Related papers

• Why AI systems don't learn and what to do about it: Lessons on autonomous learning from cognitive science [79.50667364149979]
  We critically examine the limitations of current AI models in achieving autonomous learning and propose a learning architecture inspired by human and animal cognition.<n>The proposed framework integrates learning from observation (System A) and learning from active behavior (System B) while switching flexibly between these learning modes as a function of internally generated meta-control signals (System M).
  arXiv  Detail & Related papers  (2026-03-16T14:54:56Z)
• The brain-AI convergence: Predictive and generative world models for general-purpose computation [0.0]
  Recent advances in AI systems with attention-based transformers offer a potential window into how the neocortex and cerebellum give rise to diverse functions.<n>We identify shared computational mechanisms in the attention-based neocortex and the non-attentional cerebellum.
  arXiv  Detail & Related papers  (2025-12-02T05:03:14Z)
• Intelligence Foundation Model: A New Perspective to Approach Artificial General Intelligence [55.07411490538404]
  We propose a new perspective for approaching artificial general intelligence (AGI) through an intelligence foundation model (IFM)<n>IFM aims to acquire the underlying mechanisms of intelligence by learning directly from diverse intelligent behaviors.
  arXiv  Detail & Related papers  (2025-11-13T09:28:41Z)
• Towards Neurocognitive-Inspired Intelligence: From AI's Structural Mimicry to Human-Like Functional Cognition [1.3126858950459552]
  "Neurocognitive-Inspired Intelligence" is a hybrid approach that combines neuroscience, cognitive science, computer vision, and AI.<n>These systems aim to emulate the human brain's ability to flexibly learn, reason, remember, perceive, and act in real-world settings with minimal supervision.
  arXiv  Detail & Related papers  (2025-10-09T20:10:55Z)
• WoW: Towards a World omniscient World model Through Embodied Interaction [83.43543124512719]
  Authentic physical intuition of the world model must be grounded in extensive, causally rich interactions with the real world.<n>We present WoW, a generative world model trained on 2 million robot interaction trajectories.<n>We establish WoWBench, a new benchmark focused on physical consistency and causal reasoning in video.
  arXiv  Detail & Related papers  (2025-09-26T17:59:07Z)
• Embodied AI Agents: Modeling the World [188.85697524284834]
  This paper describes our research on AI agents embodied in visual, virtual or physical forms.<n>We propose that the development of world models is central to reasoning and planning of embodied AI agents.<n>We also propose to learn the mental world model of users to enable better human-agent collaboration.
  arXiv  Detail & Related papers  (2025-06-27T16:05:34Z)
• Neural Brain: A Neuroscience-inspired Framework for Embodied Agents [78.61382193420914]
  Current AI systems, such as large language models, remain disembodied, unable to physically engage with the world.<n>At the core of this challenge lies the concept of Neural Brain, a central intelligence system designed to drive embodied agents with human-like adaptability.<n>This paper introduces a unified framework for the Neural Brain of embodied agents, addressing two fundamental challenges.
  arXiv  Detail & Related papers  (2025-05-12T15:05:34Z)
• Dynamic planning in hierarchical active inference [0.0]
  We refer to the ability of the human brain to infer and impose motor trajectories related to cognitive decisions. This study focuses on the topic of dynamic planning in active inference.
  arXiv  Detail & Related papers  (2024-02-18T17:32:53Z)
• World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges [51.92834011423463]
  We focus on the two concepts of world models and predictive coding. In neuroscience, predictive coding proposes that the brain continuously predicts its inputs and adapts to model its own dynamics and control behavior in its environment.
  arXiv  Detail & Related papers  (2023-01-14T06:38:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.