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Tactile Transfer Learning and Object Recognition With a Multifingered Hand Using Morphology Specific Convolutional

Satoshi Funabashi, Gang Yan, Fei Hongyi

    IEEE Transactions on Neural Networks and Learning Systems
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    A novel morphology-specific convolutional neural network (MS-CNN) enables robot hands with tactile sensors to recognize objects with over 95% accuracy after a single touch. This approach effectively organizes tactile data for improved robotic perception and object recognition.

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    Area of Science:

    • Robotics
    • Artificial Intelligence
    • Sensor Technology

    Background:

    • Multifingered robot hands with distributed tactile sensors enhance object exploration and recognition.
    • Convolutional neural networks (CNNs) excel at processing high-dimensional data like tactile sensor arrays.
    • Organizing diverse tactile inputs for CNNs remains a challenge for effective robotic manipulation.

    Purpose of the Study:

    • To develop a morphology-specific CNN (MS-CNN) that leverages the physical configuration of tactile sensors on a robot hand.
    • To improve the ability of robot hands to process and interpret complex tactile information for object recognition.
    • To demonstrate the effectiveness of the MS-CNN in real-world object recognition tasks.

    Main Methods:

    • Equipped a four-fingered Allegro robot hand with 240 uSkin tactile sensors, each measuring three-axis contact force.
    • Implemented a morphology-specific CNN (MS-CNN) with hierarchical convolutional layers mirroring the robot hand's sensor layout.
    • Trained the MS-CNN to process tactile data hierarchically, from local clusters to individual fingers, and then the entire hand.

    Main Results:

    • The robot hand achieved over 95% accuracy in object recognition after a single touch using the trained MS-CNN.
    • The MS-CNN demonstrated effective transfer learning, recognizing nine types of physical properties with limited new data.
    • Hierarchical processing of tactile data within the MS-CNN proved crucial for high recognition rates.

    Conclusions:

    • The developed MS-CNN architecture significantly enhances the object recognition capabilities of multifingered robot hands equipped with tactile sensors.
    • The morphology-specific approach provides a robust method for organizing and processing tactile data, leading to superior performance.
    • This research paves the way for more sophisticated robotic manipulation and physical interaction through advanced tactile sensing and AI.