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arXiv · 2610.04266

Real-Time Conformal-Seeded Hybrid Inverse Kinematics for Offset Redundant Manipulators

Duc Cuong Vu·Van Tung Nguyen·Duc Hai Nguyen·Manh Cuong Nguyen·Vu Trung Tran·Minh Nhat Vu
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Abstract

This paper presents a conformal-seeded hybrid strategy for solving inverse kinematics of offset, redundant 7-DoF robot arms of the humanoid class. Analytical inverse kinematics (AIK) provides closed-form solutions with very low computational cost. However, for offset kinematic structures, the exact closed-form solution is generally unavailable, and practical AIK must rely on an approximate or simplified kinematic model. In contrast, numerical inverse kinematics (NIK) can achieve high-precision solutions on the full kinematic model. However, its convergence is highly sensitive to initialization. To overcome these limitations, we propose a two-stage hybrid inverse kinematics framework with conformal-calibrated seed selection. First, an approximate analytical model efficiently enumerates a finite set of candidate joint solutions. Second, we rank these candidates using a lightweight learned predictor of post-refinement difficulty, wrapped by split-conformal prediction into a calibrated upper bound that serves as the selection score. The best-ranked seed is then refined using a Levenberg-Marquardt solver on the full kinematic model. The proposed method combines fast candidate generation, learned seed ranking with a calibrated difficulty bound, and accurate numerical refinement, achieving real-time performance of less than 40us and a success rate of 100% in our evaluation on reachable targets. We validate the approach through large-scale stochastic simulation across the workspace and experimental demonstrations with motion planning on a humanoid robot arm. Demonstration videos are available at https://youtu.be/aeiBmw1XRbw.

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Duc Cuong Vu, Van Tung Nguyen, Duc Hai Nguyen, Manh Cuong Nguyen, Vu Trung Tran, Minh Nhat Vu. 2026-10-03. Real-Time Conformal-Seeded Hybrid Inverse Kinematics for Offset Redundant Manipulators. https://arxiv.org/abs/2610.04266

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