Go to OpenReview Archive homepageMulti-agent Embodied AI: Advances and Future Directions
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.
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