mlsanityData to 7 Oct 2026

World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges

arXiv 2301.05832 ↗First posted 14 Jan 2023cs.RO cs.AI cs.LGcited by 35

On the map: World models → World models for navigation

Abstract (arXiv)

Creating autonomous robots that can actively explore the environment, acquire knowledge and learn skills continuously is the ultimate achievement envisioned in cognitive and developmental robotics. Their learning processes should be based on interactions with their physical and social world in the manner of human learning and cognitive development. Based on this context, in this paper, we focus on the two concepts of world models and predictive coding. Recently, world models have attracted renewed attention as a topic of considerable interest in artificial intelligence. Cognitive systems learn world models to better predict future sensory observations and optimize their policies, i.e., controllers. Alternatively, 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. Both ideas may be considered as underpinning the cognitive development of robots and humans capable of continual or lifelong learning. Although many studies have been conducted on predictive coding in cognitive robotics and neurorobotics, the relationship between world model-based approaches in AI and predictive coding in robotics has rarely been discussed. Therefore, in this paper, we clarify the definitions, relationships, and status of current research on these topics, as well as missing pieces of world models and predictive coding in conjunction with crucially related concepts such as the free-energy principle and active inference in the context of cognitive and developmental robotics. Furthermore, we outline the frontiers and challenges involved in world models and predictive coding toward the further integration of AI and robotics, as well as the creation of robots with real cognitive and developmental capabilities in the future.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). Placed in World models for navigation, one of 11 directions in World models. Builds on 87 of its 318 references that are in the map. First posted on arXiv 14 Jan 2023.

Builds on

87 of 318 references.

References · 87

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Citing papers per year: 2023: 4; 2024: 11; 2025: 14; 2026: 6.

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  1. 2023Brain-inspired Computational Intelligence via Predictive Codingcited by 30
  2. 2024Generative Emergent Communication: Large Language Model is a Collective World Modelcited by 15
  3. 2024Predictive Coding Networks and Inference Learning: Tutorial and Surveycited by 7
  4. 2025Imaginative World Modeling with Scene Graphs for Embodied Agent Navigationcited by 6
  5. 2023Learning Team-Based Navigation: A Review of Deep Reinforcement Learning Techniques for Multi-Agent Pathfindingcited by 5
  6. 2024Constructive Approach to Bidirectional Influence between Qualia Structure and Language Emergencecited by 5
  7. 2025Decentralized Collective World Model for Emergent Communication and Coordinationcited by 5
  8. 2023Correspondence learning between morphologically different robots via task demonstrationscited by 2
  9. 2024Object-centric proto-symbolic behavioural reasoning from pixelscited by 2
  10. 2025System 0/1/2/3: Quad-process theory for multi-timescale embodied collective cognitive systemscited by 2
  11. 2025Interleaved Multitask Learning with Energy Modulated Learning Progresscited by 2
  12. 2025Multifunctional physical reservoir computing in soft tensegrity robotscited by 2
23 more
  1. 2023Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costscited by 1
  2. 2024The Autonomy-Alignment Problem in Open-Ended Learning Robots: Formalising the Purpose Frameworkcited by 1
  3. 2024Cognitively Inspired Energy-Based World Modelscited by 1
  4. 2024Multi-Scenario Reasoning: Unlocking Cognitive Autonomy in Humanoid Robots for Multimodal Understandingcited by 1
  5. 2025GenAI-based Multi-Agent Reinforcement Learning towards Distributed Agent Intelligence: A Generative-RL Agent Perspectivecited by 1
  6. 2025Deep Active Inference with Diffusion Policy and Multiple Timescale World Model for Real-World Exploration and Navigationcited by 1
  7. 2024Dynamic planning in hierarchical active inferencecited by 0
  8. 2024Cross-Embodied Affordance Transfer through Learning Affordance Equivalencescited by 0
  9. 2024Foundations of Multisensory Artificial Intelligencecited by 0
  10. 2024Contextual Affordances for Safe Exploration in Robotic Scenarioscited by 0
  11. 2025Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AIcited by 0
  12. 2025Beyond Individuals: Collective Predictive Coding for Memory, Attention, and the Emergence of Languagecited by 0
  13. 2025Spiking Neural Networks for Continuous Control via End-to-End Model-Based Learningcited by 0
  14. 2025Is the `Agent' Paradigm a Limiting Framework for Next-Generation Intelligent Systems?cited by 0
  15. 2025Real-World Robot Control by Deep Active Inference With a Temporally Hierarchical World Modelcited by 0
  16. 2025Vehicle Dynamics Embedded World Models for Autonomous Drivingcited by 0
  17. 2025The brain-AI convergence: Predictive and generative world models for general-purpose computationcited by 0
  18. 2026NeuroHex: A Brain-Inspired Hex Coordinate System to Enable Highly Computationally-Efficient World Models for Continuous Online-Adaptive Learningcited by 0
  19. 2026Exploring Temporal Representation in Neural Processes for Multimodal Action Predictioncited by 0
  20. 2026DINO-Explorer: Active Underwater Discovery via Ego-Motion Compensated Semantic Predictive Codingcited by 0
  21. 2026Interoceptive machine framework: Toward interoception-inspired regulatory architectures in artificial intelligencecited by 0
  22. 2026Grounded world models in biological organisms and future embodied AIcited by 0
  23. 2026A Unifying Perspective on Causal World Models: From Observations to Representations to Structurecited by 0

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