# Large Model Empowered Embodied AI: A Survey on Decision-Making and Embodied Learning

arXiv: https://arxiv.org/abs/2508.10399 · HTML: https://mlsanity.com/paper/2508.10399/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2025.

## Built on by (most cited first, 21 of 21)

- [SafeMind: Benchmarking and Mitigating Safety Risks in Embodied LLM Agents](https://mlsanity.com/paper/2509.25885/) (2025)
- [A Step Toward World Models: A Survey on Robotic Manipulation](https://mlsanity.com/paper/2511.02097/) (2025)
- [Spark: Strategic Policy-Aware Exploration via Dynamic Branching for Long-Horizon Agentic Learning](https://mlsanity.com/paper/2601.20209/) (2026)
- [Information Capacity: Evaluating the Efficiency of Large Language Models via Text Compression](https://mlsanity.com/paper/2511.08066/) (2025)
- [Token Expand-Merge: Training-Free Token Compression for Vision-Language-Action Models](https://mlsanity.com/paper/2512.09927/) (2025)
- [Aligning Agentic World Models via Knowledgeable Experience Learning](https://mlsanity.com/paper/2601.13247/) (2026)
- [Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges](https://mlsanity.com/paper/2512.04770/) (2025)
- [See What Matters: Differentiable Grid Sample Pruning for Generalizable Vision-Language-Action Model](https://mlsanity.com/paper/2605.11817/) (2026)
- [Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI](https://mlsanity.com/paper/2510.04978/) (2025)
- [World Models as Group Actions](https://mlsanity.com/paper/2605.24578/) (2026)
- [Neuromem: A Granular Decomposition of the Streaming Lifecycle in External Memory for LLMs](https://mlsanity.com/paper/2602.13967/) (2026)
- [RoboGPT-R1: Enhancing Robot Task Planning with Reinforcement Learning](https://mlsanity.com/paper/2510.14828/) (2025)
- [Leveraging Adaptive Group Negotiation for Heterogeneous Multi-Robot Collaboration with Large Language Models](https://mlsanity.com/paper/2602.06967/) (2025)
- [Position: Embodied AI Requires a Privacy-Utility Trade-off](https://mlsanity.com/paper/2605.05017/) (2026)
- [Social Structure Matters in 3D Human-Human Interaction Generation](https://mlsanity.com/paper/2606.24255/) (2026)
- [Analytic Concept-Centric Memory for Agentic Embodied Manipulation](https://mlsanity.com/paper/2606.29774/) (2026)
- [SAGE-LLM: Towards Safe and Generalizable LLM Controller with Fuzzy-CBF Verification and Graph-Structured Knowledge Retrieval for UAV Decision](https://mlsanity.com/paper/2602.23719/) (2026)
- [Joint Learning of Experiential Rules and Policies for Large Language Model Agents](https://mlsanity.com/paper/2606.27136/) (2026)
- [Towards Trustworthy Physical Intelligence: From Theory to Practice Across Life Cycle](https://mlsanity.com/paper/2607.22877/) (2026)
- [What Language Does and What the Evidence Supports: A Functional Role Taxonomy and Evidence Audit of Language Grounding in Embodied Agents](https://mlsanity.com/paper/2608.03099/) (2026)
- [Toward Unified Robot Learning: Bridging Representation, Vision-Language-Action, and World Models](https://mlsanity.com/paper/2609.03927/) (2026)

## Abstract (arXiv)

Embodied AI aims to develop intelligent systems with physical forms capable of perceiving, decision-making, acting, and learning in real-world environments, providing a promising way to Artificial General Intelligence (AGI). Despite decades of explorations, it remains challenging for embodied agents to achieve human-level intelligence for general-purpose tasks in open dynamic environments. Recent breakthroughs in large models have revolutionized embodied AI by enhancing perception, interaction, planning and learning. In this article, we provide a comprehensive survey on large model empowered embodied AI, focusing on autonomous decision-making and embodied learning. We investigate both hierarchical and end-to-end decision-making paradigms, detailing how large models enhance high-level planning, low-level execution, and feedback for hierarchical decision-making, and how large models enhance Vision-Language-Action (VLA) models for end-to-end decision making. For embodied learning, we introduce mainstream learning methodologies, elaborating on how large models enhance imitation learning and reinforcement learning in-depth. For the first time, we integrate world models into the survey of embodied AI, presenting their design methods and critical roles in enhancing decision-making and learning. Though solid advances have been achieved, challenges still exist, which are discussed at the end of this survey, potentially as the further research directions.