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February 11, 2026Advanced Intelligent Systems0 citationsOpen Access

Worm‐Inspired Soft Robots With Modular Outfit‐Changing for Intelligent Multienvironment Adaptation

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XLXiaomin LiuHWHeng WeiZGZ A Guo

Key Points

  • The aim is to create a soft robot that can adapt to various environments using a modular design.
  • Developed a soft robot inspired by worm locomotion.
  • Incorporated pneumatically actuated segments with interchangeable external modules.
  • Performed systematic experiments to test adaptability across different terrains.
  • The robot achieved locomotion speeds of up to 21.7 mm s −1 underwater.
  • Speed varied across environments: 2.1 mm s −1 on solid surfaces and 0.4 mm s −1 in sand.
  • Adaptability is enhanced through a combination of actuation, friction, and design geometry.

Abstract

Natural organisms exhibit remarkable adaptability through structural and functional reconfiguration across environments. Inspired by this principle, we report a worm‐inspired soft robot that achieves multienvironment locomotion through a simple yet powerful modular “outfit‐changing” strategy. The robot integrates pneumatically actuated peristaltic segments with interchangeable external modules—including soft friction rings, rigid scales, and flexible skirts—that enable efficient motion on ground surfaces, within pipelines, through granular media, and underwater. Systematic experiments reveal that environmental adaptability arises from the synergistic coupling of actuation, frictional interaction, and geometry, allowing the robot to dynamically balance anchoring and driving forces in each terrain. The robot achieves speeds of up to 2.1 mm s −1 on solid surfaces, 3.4 mm s −1 in pipelines, 0.4 mm s −1 in sand, and 21.7 mm s −1 underwater, demonstrating broad locomotion versatility. This work establishes a new paradigm of embodied intelligence in soft robotics—where adaptive performance emerges from modular physical design rather than algorithmic control—and provides a scalable route toward intelligent multihabitat robotic systems for exploration, inspection, and environmental monitoring.

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Cite This Study

Liu et al. (2026) studied this question.

synapsesocial.com/papers/698c1cb3267fb587c655f581https://doi.org/10.1002/aisy.202501305
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