Multi-agent Embodied AI: Advances and Future Directions

2025-05-08

AI Summary

Existing embodied AI research predominantly focuses on static, single-agent settings, rendering it inadequate for dynamic, open environments requiring multi-agent collaborative perception, reasoning, and action. Current multi-agent embodied AI approaches remain constrained by oversimplified assumptions and lack a systematic survey or unifying framework. To address this gap, this paper presents the first comprehensive survey of multi-agent embodied AI tailored to dynamic, open environments. We formally identify and analyze three core challenges: collaborative adaptation, online learning, and joint decision-making—alongside their critical bottlenecks. Methodologically, we propose an integrated technical pathway combining deep learning, large language models, multi-agent reinforcement learning (MARL), and distributed communication modeling. Furthermore, we construct the first knowledge graph for multi-agent embodied AI, encompassing algorithms, simulation platforms, evaluation benchmarks, and real-world application cases. This work establishes a foundational theoretical framework and practical roadmap for advancing the field.

Technology Category

• Intelligent Robots: Embodied AI
• Multiagent Systems: Multiagent Learning
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Embodied artificial intelligence (Embodied AI) plays a pivotal role in the application of advanced technologies in the intelligent era, where AI systems are integrated with physical bodies that enable them to perceive, reason, and interact with their environments. Through the use of sensors for input and actuators for action, these systems can learn and adapt based on real-world feedback, allowing them to perform tasks effectively in dynamic and unpredictable environments. As techniques such as deep learning (DL), reinforcement learning (RL), and large language models (LLMs) mature, embodied AI has become a leading field in both academia and industry, with applications spanning robotics, healthcare, transportation, and manufacturing. However, most research has focused on single-agent systems that often assume static, closed environments, whereas real-world embodied AI must navigate far more complex scenarios. In such settings, agents must not only interact with their surroundings but also collaborate with other agents, necessitating sophisticated mechanisms for adaptation, real-time learning, and collaborative problem-solving. Despite increasing interest in multi-agent systems, existing research remains narrow in scope, often relying on simplified models that fail to capture the full complexity of dynamic, open environments for multi-agent embodied AI. Moreover, no comprehensive survey has systematically reviewed the advancements in this area. As embodied AI rapidly evolves, it is crucial to deepen our understanding of multi-agent embodied AI to address the challenges presented by real-world applications. To fill this gap and foster further development in the field, this paper reviews the current state of research, analyzes key contributions, and identifies challenges and future directions, providing insights to guide innovation and progress in this field.

Problem

• Addressing multi-agent collaboration in dynamic environments
• Overcoming simplified models in embodied AI research
• Providing a comprehensive survey of multi-agent embodied AI

Innovation

• Multi-agent systems for dynamic open environments
• Integration of deep and reinforcement learning
• Real-time collaborative problem-solving mechanisms