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Visual Agnosia01:12

Visual Agnosia

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Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round...
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Prosopagnosia01:24

Prosopagnosia

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Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
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Vision01:24

Vision

54.9K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Related Experiment Video

Updated: Aug 23, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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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
    |November 3, 2022
    PubMed
    Summary

    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.