AI-embodied multi-modal flexible electronic robots with programmable sensing, actuating and self-learning

Junfeng Li, Zhangyu Xu, Nanpei Li, Kaijun Zhang, Guangyong Xiong, Minjie Sun, Chao Hou, Jingjing Ji, Fan Zhang (), Junwen Zhong () and YongAn Huang ()
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Junfeng Li: Wuhan University of Technology
Zhangyu Xu: Huazhong University of Science and Technology
Nanpei Li: Wuhan University of Technology
Kaijun Zhang: University of Macau
Guangyong Xiong: Wuhan University of Technology
Minjie Sun: Wuhan University of Technology
Chao Hou: Huazhong University of Science and Technology
Jingjing Ji: Huazhong University of Science and Technology
Fan Zhang: Huazhong University of Science and Technology
Junwen Zhong: University of Macau
YongAn Huang: Huazhong University of Science and Technology

Nature Communications, 2025, vol. 16, issue 1, 1-10

Abstract: Abstract Achieving robust environmental interaction in small-scale soft robotics remains challenging due to limitations in terrain adaptability, real-time perception, and autonomous decision-making. Here, we introduce Flexible Electronic Robots constructed from programmable flexible electronic components and setae modules. The integrated platform combines multimodal sensing/actuation with embedded computing, enabling adaptive operation in diverse environments. Applying modular design principles to configure structural topologies, actuation sequences, and circuit layouts, these robots achieve multimodal locomotion, including vertical surface traversal, directional control, and obstacle navigation. The system implements proprioception (shape and attitude) and exteroception (vision, temperature, humidity, proximity and pathway shape recognition) under dynamic conditions. Onboard computational units enable autonomous behaviors like hazard evasion and thermal gradient tracking through adaptive decision-making, supported by embodied artificial intelligence. In this work, we establish a framework for creating small-scale soft robots with enhanced environmental intelligence through tightly integrated sensing, actuation, and decision-making architectures.

Date: 2025
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DOI: 10.1038/s41467-025-63881-6

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