Year: 2025
Type: article
Abstract: Recent robotics advancements have accelerated the development of intelligent manufacturing systems. Therein, object recognition with visual sensing plays a critical role. However, liquid classification in containers remains an untapped region due to the optical transparency of liquids and the similarity of their tactile properties. Here, we introduce a multimodal tactile sensor system on robotic fingers with machine learning for classifying liquids in bottles. Specifically, the sensor system integrates perceptions of thermal conduction and frequency response, inspired by human thermal and vibrational receptors. Additionally, a convolutional neural network with dual parallel structure is employed to process the multimodal input. The multimodal system demonstrates two different classification tasks: classifying water volumes and liquid types. The proposed model achieves high classification accuracy in both tasks and enables real-time operation. This biologically inspired approach offers a solution for liquid classification using static contact, with broad application in robotic perception.
Source: npj Robotics
Authors: Hyunjun Park, Youngsu Cha
Institution: Korea University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,990
Award: G106031818
OpenAlex ID: W4414207446