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Related Concept Videos

Deglutition01:25

Deglutition

Swallowing, otherwise known as deglutition, facilitates the transport of food from the mouth to the stomach. It is a multifaceted process that involves both the tongue and the muscles of the throat and esophagus. Saliva and mucus aid in this process, which takes approximately 4 to 8 seconds for semi-solid or solid food and around 1 second for liquids or very soft food.
Swallowing can be divided into three stages: the voluntary phase, the pharyngeal phase, and the esophageal phase. Although the...

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Bioinspired Swallowing Soft Gripper with Toroidal Optical Waveguides for Multimodal Interactive Perception.

Sheng Xiang1, Muye Niu1, Wenjie Fang1

  • 1School of Automation, CICAEET, Nanjing University of Information Science and Technology, Nanjing, China.

Soft Robotics
|May 29, 2026
PubMed
Summary

This study introduces a novel soft gripper inspired by sea anemones, utilizing optical waveguides for advanced sensing. It enables adaptive grasping and detailed object perception, enhancing robotic capabilities in delicate handling tasks.

Keywords:
machine learningmultimodal soft gripperoptical waveguidesproprioception

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Area of Science:

  • Robotics
  • Materials Science
  • Biomimetics

Background:

  • Integrating sensors into soft grippers for multimodal sensing under deformation is challenging.
  • Existing soft grippers lack continuous proprioceptive feedback and adaptive grasping strategies for diverse objects.

Purpose of the Study:

  • To develop a multimodal soft gripper with integrated optical waveguides for continuous proprioceptive feedback.
  • To enable adaptive grasping strategies including contacting, expansion, and swallowing modes.
  • To achieve real-time perception of object properties like shape, hardness, and texture.

Main Methods:

  • Integration of eight toroidal optical waveguides into a soft gripper structure.
  • Utilizing deformation-induced optical attenuation processed by machine-learning pipelines for sensing.
  • Implementing mode-specific sensing on outer, inner, and bottom interfaces of the gripper.

Main Results:

  • Achieved a detection limit of 0.04 N, resolution of 0.006 N, and response time of 55 ms.
  • Machine-learning classification demonstrated >89% accuracy in object property perception.
  • Successfully demonstrated multi-object swallowing and a breakfast task involving fragile item handling.

Conclusions:

  • The developed soft gripper offers mode-switching interactive perception capabilities.
  • This design expands opportunities for soft robotics in fragile product handling and laboratory automation.
  • The biomimetic approach provides a novel solution for proprioceptive feedback in soft robotic systems.