Explainable Neural Inverse Kinematics for Obstacle-Aware Robotic Manipulation: A Comparative Analysis of IKNet Variants

Neural networks can now enable real-time robot control, but their "black box" nature creates safety risks—this study addresses that gap by making AI decision-making in robotic arms transparent and interpretable for regulatory compliance.

Explainability correlates with physical safety: Networks that distributed decision-making more evenly across input variables (robot position and orientation data) maintained wider safety margins around obstacles, suggesting that understanding how an AI makes decisions can directly improve real-world safety outcomes.

Simpler, more focused AI models can be safer: The study's "Focused IKNet" variant, which separated position and orientation processing, achieved comparable accuracy to more complex models while potentially improving interpretability—demonstrating that responsible AI doesn't require sacrificing performance.

Visualization tools make AI decisions actionable for non-experts: Using heat maps and partial-dependence plots, the researchers made neural network decision patterns visible to engineers and safety personnel, bridging the gap between AI researchers and those responsible for deploying robots safely in real environments.

Synthetic testing can validate AI safety before deployment: By testing networks in simulated obstacle scenarios before real-world use, the team demonstrated a practical workflow for verifying that explainable AI techniques actually translate to safer robotic behavior—not just theoretical improvements.