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This paper proposes a novel inverse kinematics (IK) solver of articulatedrobotic systems for path planning. IK is a traditional but essential problemfor robot manipulation. Recently, data-driven methods have been proposed toquickly solve the IK for path planning. These methods can handle a large amountof IK requests at once with the advantage of GPUs. However, the accuracy isstill low, and the model requires considerable time for training. Therefore, wepropose an IK solver that improves accuracy and memory efficiency by utilizingthe continuous hidden dynamics of Neural ODE. The performance is compared usingmultiple robots.

NODE IK: Solving Inverse Kinematics with Neural Ordinary Differential Equations for Path Planning