Singularity Avoidance in Inverse Kinematics: A Unified Treatment of Classical and Learning-based Methods

TL;DR

This paper unifies classical and learning-based inverse kinematics methods to address singularity issues, introduces a benchmarking protocol, and evaluates 12 solvers on a robotic arm to compare their robustness and accuracy.

Contribution

It provides a comprehensive taxonomy, a benchmarking protocol, and experimental evaluation of classical, learning-based, and hybrid IK methods for singularity avoidance.

Findings

Pure learning methods often fail on well-conditioned targets.

Hybrid methods like IKFlow and CycleIK significantly improve robustness.

Classical refinement enhances the performance of learned IK solvers.

Abstract

Singular configurations cause loss of task-space mobility, unbounded joint velocities, and solver divergence in inverse kinematics (IK) for serial manipulators. No existing survey bridges classical singularity-robust IK with rapidly growing learning-based approaches. We provide a unified treatment spanning Jacobian regularization, Riemannian manipulability tracking, constrained optimization, and modern data-driven paradigms. A systematic taxonomy classifies methods by retained geometric structure and robustness guarantees (formal vs. empirical). We address a critical evaluation gap by proposing a benchmarking protocol and presenting experimental results: 12 IK solvers are evaluated on the Franka Panda under position-only IK across four complementary panels measuring error degradation by condition number, velocity amplification, out-of-distribution robustness, and computational cost. Results show that pure learning methods fail even on well-conditioned targets (MLP: 0% success, approx. 10 mm mean error), while hybrid warm-start architectures - IKFlow (59% to 100%), CycleIK(0% to 98.6%), GGIK (0% to 100%) - rescue learned solvers via classical refinement, with DLS converging from initial errors up to 207 mm. Deeper singularity-regime evaluation is identified as immediate future work.