Tactile Object Recognition With Recurrent Neural Networks Through a Perceptive Soft Gripper

Soft robot perception integrates information from distributed, multi-modal sensors, broadening their application to active interaction. Our work introduces recurrent learning models for tactile-based object recognition, demonstrating comparable performance in virtual and real-world scenarios. The wo...

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Veröffentlicht in:	IEEE robotics and automation letters Jg. 10; H. 7; S. 7023 - 7030
Hauptverfasser:	Donato, Enrico, Pelliccia, David, Hosseinzadeh, Matin, Amiri, Mahmood, Falotico, Egidio
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.07.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Classification algorithms Fingers Grippers Long short term memory multi-modal integration Object recognition Recurrent neural networks Robot sensing systems Sensors soft gripper Soft robotics Spatial distribution Tactile sensing Training
ISSN:	2377-3766, 2377-3766
Online-Zugang:	Volltext
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