Multi-agent systems (MASs) have demonstrated significant achievements in a wide range of tasks, leveraging their capacity for coordination and adaptation within complex environments. Moreover, the enhancement of their intelligent functionalities is crucial for tackling increasingly challenging tasks. This goal resonates with a paradigm shift within the artificial intelligence (AI) community, from “internet AI” to “embodied AI”, and the MASs with embodied AI are referred to as embodied multi-agent systems (EMASs). An EMAS has the potential to acquire generalized competencies through interactions with environments, enabling it to effectively address a variety of tasks and thereby make a substantial contribution to the quest for artificial general intelligence. Despite the burgeoning interest in this domain, a comprehensive review of EMAS has been lacking. This paper offers analysis and synthesis for EMASs from a control perspective, conceptualizing each embodied agent as an entity equipped with a “brain” for decision and a “body” for environmental interaction. System designs are classified into open-loop, closed-loop, and double-loop categories, and EMAS implementations are discussed. Additionally, the current applications and challenges faced by EMASs are summarized and potential avenues for future research in this field are provided.
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