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

arXiv: https://arxiv.org/abs/2209.00498 · HTML: https://mlsanity.com/paper/2209.00498/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2022.

## Built on by (most cited first, 3 of 3)

- [Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints](https://mlsanity.com/paper/2306.09739/) (2023)
- [Inverse Kinematics for Neuro-Robotic Grasping with Humanoid Embodied Agents](https://mlsanity.com/paper/2404.08825/) (2024)
- [Exploring the Intrinsic Geometry of Diffusion Models with Constrained Inverse Kinematics](https://mlsanity.com/paper/2606.26408/) (2026)

## Abstract (arXiv)

This paper proposes a novel inverse kinematics (IK) solver of articulated robotic systems for path planning. IK is a traditional but essential problem for robot manipulation. Recently, data-driven methods have been proposed to quickly solve the IK for path planning. These methods can handle a large amount of IK requests at once with the advantage of GPUs. However, the accuracy is still low, and the model requires considerable time for training. Therefore, we propose an IK solver that improves accuracy and memory efficiency by utilizing the continuous hidden dynamics of Neural ODE. The performance is compared using multiple robots.