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

TL;DR

This paper introduces an explainability workflow for neural inverse kinematics models, combining Shapley-value attribution with obstacle avoidance evaluation to enhance safety and transparency in robotic manipulation.

Contribution

It presents a novel explainability framework for IK neural networks, integrating attribution methods with obstacle-aware evaluation to improve trustworthiness and safety.

Findings

Distributed importance correlates with wider safety margins.

Explainability reveals hidden failure modes.

Architectural choices impact safety and accuracy balance.

Abstract

Deep neural networks have accelerated inverse-kinematics (IK) inference to the point where low cost manipulators can execute complex trajectories in real time, yet the opaque nature of these models contradicts the transparency and safety requirements emerging in responsible AI regulation. This study proposes an explainability centered workflow that integrates Shapley-value attribution with physics-based obstacle avoidance evaluation for the ROBOTIS OpenManipulator-X. Building upon the original IKNet, two lightweight variants-Improved IKNet with residual connections and Focused IKNet with position-orientation decoupling are trained on a large, synthetically generated pose-joint dataset. SHAP is employed to derive both global and local importance rankings, while the InterpretML toolkit visualizes partial-dependence patterns that expose non-linear couplings between Cartesian poses and joint angles. To bridge algorithmic insight and robotic safety, each network is embedded in a simulator that subjects the arm to randomized single and multi-obstacle scenes; forward kinematics, capsule-based collision checks, and trajectory metrics quantify the relationship between attribution balance and physical clearance. Qualitative heat maps reveal that architectures distributing importance more evenly across pose dimensions tend to maintain wider safety margins without compromising positional accuracy. The combined analysis demonstrates that explainable AI(XAI) techniques can illuminate hidden failure modes, guide architectural refinements, and inform obstacle aware deployment strategies for learning based IK. The proposed methodology thus contributes a concrete path toward trustworthy, data-driven manipulation that aligns with emerging responsible-AI standards.