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    <titleInfo>
        <title>Data-driven optimization for underactuated robotic hands</title>
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    <name type="personal">
        <namePart type="family">Ciocarlie</namePart>
        <namePart type="given">Matei</namePart>
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        <affiliation>Columbia University. Computer Science</affiliation>
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    <name type="personal" ID="pka1">
        <namePart type="family">Allen</namePart>
        <namePart type="given">Peter K.</namePart>
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        <affiliation>Columbia University. Computer Science</affiliation>
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    <abstract>Passively adaptive and underactuated robotic hands have shown the potential to achieve reliable grasping in unstructured environments without expensive mechanisms or sensors. Instead of complex run-time algorithms, such hands use design-time analysis to improve performance for a wide range of tasks. Along these directions, we present an optimization framework for underactuated compliant hands. Our approach uses a pre-defined set of grasps in a quasistatic equilibrium formulation to compute the actuation mechanism design parameters that provide optimal performance. We apply our method to a class of tendon-actuated hands; for the simplified design of a two-fingered gripper, we show how a global optimum for the design optimization problem can be computed. We have implemented the results of this analysis in the construction of a gripper prototype, capable of a wide range of grasping tasks over a variety of objects.</abstract>
    <subject>
        <topic>Robotics</topic>
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            <title>2010 IEEE International Conference on Robotics and Automation</title>
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            <publisher>IEEE</publisher>
            <dateIssued encoding="w3cdtf">2010</dateIssued>
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                <start>1292</start>
                <end>1299</end>
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        <identifier type="doi">http://dx.doi.org/10.1109/ROBOT.2010.5509793</identifier>
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    <identifier type="hdl">http://hdl.handle.net/10022/AC:P:15086</identifier>
    
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