HJCD-IK: GPU-Accelerated Inverse Kinematics through Batched Hybrid Jacobian Coordinate Descent
This work addresses the speed-accuracy trade-off in inverse kinematics for robotics, providing a practical solver that outperforms existing methods on both metrics.
HJCD-IK introduces a GPU-accelerated hybrid IK solver that combines orientation-aware coordinate descent with Jacobian-based polishing, achieving up to order-of-magnitude gains in speed and accuracy over state-of-the-art solvers and consistently finding collision-free solutions on the accuracy-latency Pareto frontier.
Inverse Kinematics (IK) is a core problem in robotics, in which joint configurations are found to achieve a (collision-free) desired end-effector pose. Modern IK solvers face a fundamental trade-off: analytical methods are fast but lack generality, while numerical optimization-based methods are broadly applicable but prone to local minima and high computational costs. To overcome this challenge, 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.