ArticleMicrosystems & nanoengineering2026
Intelligent soft robotic gripper for non-destructive grasping and attribute recognition via multi-modal waveguide tactile sensors.
Article in Microsystems & nanoengineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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Abstract
The intelligent soft robotic gripper integrated with tactile sensors significantly enhances the robot's execution capabilities in complex tasks, resolving critical shortcomings of traditional mechanical grippers-namely, fragile item breakage from rigid impacts, irregular object slippage, and inefficiency due to recognition errors. While electrical sensors (e.g., piezoresistive, capacitive) struggle with structural complexity, signal crosstalk, and environmental interference, optical waveguide tactile sensing offers superior sensitivity, rapid dynamics, and electromagnetic immunity. However, existing waveguide tactile systems face two key limitations: millimeter-scale waveguides cause beam divergence, limiting deformation sensitivity and complicating heterogeneous integration. Additionally, critical gaps remain in adaptive grasping control and contextual object recognition during manipulation. Herein, we present a soft robotic gripper integrated with slender elastic optical waveguide sensors (EOWS) and equipped with a closed-loop feedback control module to achieve intelligent grasping and object attribute recognition. The hand comprises three flexible silicone fingers, each finger seamlessly integrates three EOWS for multi-modal tactile sensing. These sensors exhibit high sensitivity to bending angle (0.273%/°), contact force (0.843%/N), and pressure (1.064%/N). Furthermore, a PID adaptive grasping control strategy and a long short-term memory (LSTM) deep learning algorithm are introduced to dynamically adjust the grasping force and intelligently recognize object attributes such as shape, size, and hardness, with accuracies exceeding 97% for each attribute. Ultimately, experimental validation via a smart fruit-sorting system highlights the platform's potential for precision agriculture, intelligent logistics, and medical robotics, demonstrating robust, adaptive manipulation in real-world applications. We present a soft robotic gripper seamlessly integrated with slender multi-modal elastic optical waveguide sensors (EOWS) and equipped with an adaptive control module to achieve intelligent grasping and object attribute recognition. Experimental validation via a smart fruit-sorting system highlights the platform's potential for precision agriculture, intelligent logistics, and medical robotics, demonstrating robust, adaptive manipulation in real-world applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.