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Robotic Kinesthesia: Estimating Object Geometry and Material With Robot's Haptic Senses.

Seung-Chan Kim, Semin Ryu

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    Robotic systems can now identify object shape and material using haptic sensing, mimicking human touch. This breakthrough uses joint torque data for advanced material and geometry recognition.

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    Area of Science:

    • Robotics
    • Artificial Intelligence
    • Haptic Sensing

    Background:

    • Humans possess advanced tactile sensing capabilities for object recognition.
    • Current robotic systems often lack sophisticated haptic feedback for material and shape determination.

    Purpose of the Study:

    • To develop a robotic system capable of jointly learning object shape and material properties using haptic sensing.
    • To emulate human tactile recognition abilities in artificial systems.

    Main Methods:

    • A serially connected robotic arm equipped with joint torque sensors was utilized.
    • A supervised learning approach was employed to classify surface geometry and material types from multivariate time-series sensor data.
    • A joint torque-to-position generation task was developed to infer surface profiles from torque measurements.

    Main Results:

    • Experimental validation confirmed the effectiveness of the proposed torque-based classification and regression tasks.
    • The system successfully recognized material types and object geometry using haptic feedback from joint torque sensors.
    • The derived one-dimensional surface profiles accurately represented object geometry.

    Conclusions:

    • Robotic systems can effectively utilize haptic sensing, specifically joint torque data, for object material and geometry recognition.
    • The proposed method offers a pathway to developing robots with human-like tactile perception.
    • This research advances the field of artificial tactile sensing and robotic manipulation.