eess.SPOct 1, 2026

Token Communication-Assisted Collaborative Embodied Artificial Intelligence: Concepts, Framework, and Opportunities

Authors: Peng Yi, Ying-Chang Liang

Organizations: National Key Laboratory of Wireless Communications, and also with the Center for Intelligent Networking and Communications (CINC), University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China · Institute of Fundamental and Frontier Sciences and the Center for Intelligent Networking and Communications (CINC), University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China

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Abstract

Collaborative embodied artificial intelligence (CEAI) enables multiple physical agents to perceive, reason, and act cooperatively in dynamic environments. Effective communication is essential for CEAI, yet CEAI agents must exchange not only large multimodal observations but also task-relevant insights, intents, and interactive information over long horizons. This article investigates token communication (TokCom) as a native intelligence interface for CEAI, in which tokens serve jointly as compact semantic carriers for communication and fundamental inference units for generative foundation models (GFMs). We first discuss how TokCom supports insight sharing, intent alignment, and interactive control among embodied agents. We then propose a TokCom-assisted CEAI framework driven by a task-adaptive communication protocol. Comprising a compact codebook, syntax rules, and contextual examples, this protocol guides GFM-based transceivers to distill messages into compact tokens and reconstruct them after wireless transmission. A case study on collaborative object transport demonstrates that the proposed TokCom framework substantially reduces the source payload bit consumption while preserving task efficiency and showing robustness under noisy channels. Finally, we outline future research directions.

Figures & tables

Fig. 1: A conceptual framework for CEAI. The figure illustrates three key components. Left: A diverse array of embodied intelligent agents. Center: The core capabilities of embodied intelligence, anchored by GFMs. These capabilities, including perception, memory, reasoning, communication, and action, drive advanced behaviors such as environmental interaction, multi-agent collaboration, and continuous evolution, ultimately fostering symbiosis with the physical world. Right: The requisite evolution of communication paradigms, shifting the focus from simple connectivity to intelligent collaboration.
Fig. 2: TokCom capabilities in CEAI across insight sharing, intent alignment, and interactive control.
Fig. 3: Proposed TokCom-assisted CEAI framework. Part A presents the embodied agent system, including perception, memory, reasoning, action, and communication modules. The proposed components are highlighted in Parts B and C: Part B illustrates task-adaptive protocol initialization, while Part C presents protocol-based communication, in which agents encode natural-language messages into protocol tokens and decode the received tokens for subsequent inference.
Fig. 4: Simulation environment and results. Part A shows the room layout, target objects, and containers. Parts B(a) and B(b) report TS and BC for food and stuff tasks, respectively. Part B(c) compares average TS and BC across schemes. Parts B(d) and B(e) show sentence similarity and TS versus SNR.

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