🤖 AI Summary
This work addresses the challenges posed by singular configurations in inverse kinematics for serial manipulators—such as loss of task-space mobility, unbounded joint velocities, and solver divergence—by proposing a unified framework that integrates Jacobian regularization, Riemannian manipulability tracking, constrained optimization, and data-driven techniques. It establishes, for the first time, a systematic taxonomy bridging classical robust inverse kinematics and learning-based approaches, categorizing existing methods according to the geometric structures they preserve and the nature of their robustness guarantees, whether formal or empirical. Evaluation of twelve solvers on the Franka Panda platform demonstrates that purely learning-based methods exhibit high failure rates, whereas hybrid architectures employing classical methods for refinement achieve significantly higher success rates of 98.6%–100%, thereby validating the efficacy and superiority of the proposed framework.
📝 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.