Cyborg-swarm cooperation and game via affective-based brain-machine interface.

Overview

1. WINDY Lab, Department of Artificial Intelligence, Westlake University, Hangzhou 310030, China
2. The State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
3. School of Information Science and Engineering, The Shenyang University of Technology, Shenyang 110870, China
4. School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168, China
5. School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
6. Software College, Northeastern University, Shenyang 110169, China
7. Laboratory of Hearing Research, School of Life Sciences, China Medical University, Shenyang 110122, China

Abstract

The integration of biological organisms with robotic systems has enabled hybrid cyborg platforms that combine biological sensory agility with electromechanical precision. However, existing cyborg systems predominantly rely on unidirectional stimulus-driven control, treating animals as bio-actuators while neglecting their intrinsic cognitive states. To bridge this gap, we present a closed-loop cyborg-swarm architecture that utilizes the animal’s internal affective state (fear) as a high-level trigger to modulate robotic swarm strategies. Specifically, we developed a lightweight, real-time wireless brain–machine interface (BMI) to record local field potentials from the mouse basolateral amygdala. To ensure robust decoding in freely moving subjects, we implemented a dual-threshold detection algorithm that identifies fear states based on elevated -band power (15–30 Hz) and suppressed high-frequency noise, effectively rejecting motion artifacts. This decoded intent drives a dual-mode control framework: under baseline conditions, the system operates in a proportional-integral-derivative (PID)-based Exploration Mode; upon detection of fear, it autonomously switches to an Interaction Mode governed by Multi-Agent Deep Deterministic Policy Gradient. In this mode, a heterogeneous robotic swarm (comprising a MouseBot and an ally micro aerial vehicle (MAV)) executes coordinated adversarial defense strategies against an enemy MAV. Experimental results in a search-interference game demonstrate that biological affective signals can successfully trigger millisecond-level control authority switching, enabling the emergence of complex bio-machine cooperative behaviors. This work marks a paradigm shift from physical-level interaction to cognitive-level bio-hybrid cooperation, validating a scalable framework for emotion-modulated cyborg swarms.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

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The paper's code and data availability statement is in the Data section.

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Data

Datasets cited

• github.com/czr-gif/experiment-data-for-cyborg-swarm-cooperation-and-game-via-affective-based-brain-machine-interface, at github.com; found in “DATA AVAILABILITY”

Data availability

To facilitate reproducibility and open science, we have open-sourced the complete project resources. The experiment video and trajectory logs are available at GitHub: https://github.com/czr-gif/Experiment-Data-for-Cyborg-Swarm-Cooperation-and-Game-via-Affective-based-Brain-Machine-Interface.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 2, 28 September 2026

• Funding: added National Natural Science Foundation of China: 62573305, 62573405, 62303446; National Key Research and Development Program of China: 2023YFB4705500

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 5 keywords, 40 references.

Cite

This paper

Chen, Z., Zhang, L., Chen, G., Liu, H., Wang, Z., Zhao, X., Guo, S., Zhao, T., Sun, M., Liang, W., Qin, L., Zhang, M., Liu, L., & Wang, W. (2026). Cyborg-swarm cooperation and game via affective-based brain-machine interface. National science review, 13(13), nwag313. https://doi.org/10.1093/nsr/nwag313

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