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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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Learning Haptic-Based Object Pose Estimation for In-Hand Manipulation Control With Underactuated Robotic Hands.

Osher Azulay, Inbar Ben-David, Avishai Sintov

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    Summary
    This summary is machine-generated.

    This study introduces a haptic-based approach for robotic hands, using kinesthetic and tactile sensing for object pose estimation and manipulation, overcoming limitations of vision-based methods in occluded environments.

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    Area of Science:

    • Robotics
    • Haptic Sensing
    • Underactuated Systems

    Background:

    • Underactuated compliant robotic hands present modeling challenges, often relying on vision for pose estimation.
    • Visual perception is limited in occluded or partially occluded environments, hindering robotic manipulation.
    • Existing methods struggle with uncertainty and environmental limitations.

    Purpose of the Study:

    • To explore the use of haptics (kinesthetic and tactile sensing) for pose estimation and in-hand manipulation with underactuated robotic hands.
    • To develop a vision-free feature state representation using low-cost hardware.
    • To enable robust object manipulation in environments where visual line-of-sight is compromised.

    Main Methods:

    • Development of a low-cost, 3D-printable flexible tactile sensor for implicit contact information.
    • Analysis of kinesthetic and tactile features with various regression models for pose prediction using a two-finger underactuated hand.
    • Implementation of a Model Predictive Control (MPC) approach for haptics-only object manipulation.

    Main Results:

    • Successful pose estimation for objects with diverse geometry, stiffness, and texture.
    • Demonstration of accurate object manipulation to desired workspace goals using solely haptic feedback.
    • Validation of the proposed haptic approach in mitigating occluded environment challenges.

    Conclusions:

    • Haptic sensing offers a viable alternative to vision for pose estimation and manipulation with underactuated robotic hands.
    • The proposed low-cost tactile sensor and MPC framework enable robust, vision-free in-hand manipulation.
    • This research advances robotic capabilities in complex and occluded environments.