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    <title>Inverse Kinematics on A²R Lab</title>
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    <description>Recent content in Inverse Kinematics on A²R Lab</description>
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    <copyright>&amp;copy; {year} Brian Plancher</copyright>
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      <title>HJCD-IK: GPU-Accelerated Inverse Kinematics through Batched Hybrid Jacobian Coordinate Descent</title>
      <link>https://a2r-lab.github.io/publication/hjcdik/</link>
      <pubDate>Wed, 30 Sep 2026 00:00:00 +0000</pubDate>
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      <description>We introduce HJCD-IK, a GPU-accelerated, sampling-based hybrid solver. By pairing a novel orientation-aware greedy coordinate descent initialization with Jacobian-based polishing and a parallel collision filter, our method achieves up to order-of-magnitude gains in speed and accuracy over state-of-the-art solvers, consistently finding collision-free solutions on the accuracy-latency Pareto frontier, while producing a diverse distribution of high-quality samples. We validate our solver on a physical Franka manipulator and release our code open-source.</description>
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