mlsanityData to 7 Oct 2026

A call for embodied AI

arXiv 2402.03824 ↗First posted 6 Feb 2024cs.AIcited by 7

Not on the map. Why?

Abstract (arXiv)

We propose Embodied AI as the next fundamental step in the pursuit of Artificial General Intelligence, juxtaposing it against current AI advancements, particularly Large Language Models. We traverse the evolution of the embodiment concept across diverse fields - philosophy, psychology, neuroscience, and robotics - to highlight how EAI distinguishes itself from the classical paradigm of static learning. By broadening the scope of Embodied AI, we introduce a theoretical framework based on cognitive architectures, emphasizing perception, action, memory, and learning as essential components of an embodied agent. This framework is aligned with Friston's active inference principle, offering a comprehensive approach to EAI development. Despite the progress made in the field of AI, substantial challenges, such as the formulation of a novel AI learning theory and the innovation of advanced hardware, persist. Our discussion lays down a foundational guideline for future Embodied AI research. Highlighting the importance of creating Embodied AI agents capable of seamless communication, collaboration, and coexistence with humans and other intelligent entities within real-world environments, we aim to steer the AI community towards addressing the multifaceted challenges and seizing the opportunities that lie ahead in the quest for AGI.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 56 of its 146 references that are in the map. First posted on arXiv 6 Feb 2024.

Builds on

56 of 146 references.

References · 56

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  4. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  5. 2020Scaling Laws for Neural Language Modelscited by 6,411
  6. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  7. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
  8. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  9. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  10. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  11. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,422
  12. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
38 more
  1. 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,313
  2. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,242
  3. 2017Curiosity-driven Exploration by Self-supervised Predictioncited by 1,173
  4. 2017Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environmentscited by 1,013
  5. 2023A Comprehensive Survey of Continual Learning: Theory, Method and Applicationcited by 860
  6. 2017Deep Reinforcement Learning that Matterscited by 727
  7. 2018VirtualHome: Simulating Household Activities via Programscited by 411
  8. 2021Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learningcited by 395
  9. 2017Measuring Catastrophic Forgetting in Neural Networkscited by 359
  10. 2021A Survey of Embodied AI: From Simulators to Research Taskscited by 256
  11. 2023AI Alignment: A Comprehensive Surveycited by 256
  12. 2020ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulationcited by 249
  13. 2023Large Language Model Alignment: A Surveycited by 241
  14. 2021iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Taskscited by 191
  15. 2020A Review of Generalized Zero-Shot Learning Methodscited by 159
  16. 2017Robust Deep Reinforcement Learning with Adversarial Attackscited by 152
  17. 2018Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Taskscited by 150
  18. 2017Generalization in Deep Learningcited by 146
  19. 2017Domain Adaptation for Visual Applications: A Comprehensive Surveycited by 127
  20. 2019Learning Exploration Policies for Navigationcited by 113
  21. 2021Towards artificial general intelligence via a multimodal foundation modelcited by 112
  22. 2024AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agentscited by 83
  23. 2020An Exploration of Embodied Visual Explorationcited by 56
  24. 2023Generalized Teacher Forcing for Learning Chaotic Dynamicscited by 55
  25. 2019Auditing Radicalization Pathways on YouTubecited by 52
  26. 2022Skill-based Model-based Reinforcement Learningcited by 50
  27. 2018CHALET: Cornell House Agent Learning Environmentcited by 38
  28. 2024HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environmentscited by 32
  29. 2023Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoningcited by 30
  30. 2019VRKitchen: an Interactive 3D Virtual Environment for Task-oriented Learningcited by 29
  31. 2022The free energy principle made simpler but not too simplecited by 27
  32. 2019A Deeper Look at Facial Expression Dataset Biascited by 25
  33. 2021Technological Approach to Mind Everywhere (TAME): an experimentally-grounded framework for understanding diverse bodies and mindscited by 25
  34. 2021Removing biased data to improve fairness and accuracycited by 17
  35. 2020Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate datacited by 16
  36. 2022Emblaze: Illuminating Machine Learning Representations through Interactive Comparison of Embedding Spacescited by 16
  37. 2023Mortal Computation: A Foundation for Biomimetic Intelligencecited by 15
  38. 2021Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose?cited by 11

50 most cited of 56 All 56 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 2; 2026: 1.

7 papers.

  1. 2025Neural Brain: A Neuroscience-inspired Framework for Embodied Agentscited by 10
  2. 2025Thinking Beyond Tokens: From Brain-Inspired Intelligence to Cognitive Foundations for Artificial General Intelligence and its Societal Impactcited by 6
  3. 2024PAVLM: Advancing Point Cloud based Affordance Understanding Via Vision-Language Modelcited by 4
  4. 2024Stem-OB: Generalizable Visual Imitation Learning with Stem-Like Convergent Observation through Diffusion Inversioncited by 4
  5. 2024How Do Artificial Intelligences Think? The Three Mathematico-Cognitive Factors of Categorical Segmentation Operated by Synthetic Neuronscited by 3
  6. 2024Multimodal Datasets and Benchmarks for Reasoning about Dynamic Spatio-Temporality in Everyday Environmentscited by 1
  7. 2026Physical Transformercited by 0