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

2025-12-29

AI Summary

To address the challenge of simultaneously ensuring interpretability and safety in real-time inverse kinematics (IK) for deep neural networks, this paper proposes an explainable AI (XAI) framework integrating SHAP-based attribution analysis with capsule-based geometric collision verification. The framework introduces a novel workflow jointly leveraging Shapley value attribution and physics-informed collision detection. We design two lightweight IKNet variants—residual-enhanced and pose-decoupled—to systematically analyze the correlation between attribution distribution balance and safety margin. Evaluated in multi-obstacle environments, our method achieves a 98.2% collision-free trajectory execution rate, improves average safety distance by 37% over baseline methods, and attains end-effector positioning accuracy of <2.1 mm RMSE. The approach ensures real-time inference while delivering high interpretability, formal safety guarantees, and millimeter-level control precision.

Technology Category

Natural Language Processing: Safety and RobustnessPhilosophy and Ethics of AI: Safety, Robustness & TrustworthinessHumans and AI: Explainable AI (XAI) for Human Understanding

Application Category

Responsible Web: Machine-in-the-loop, human agency and autonomySearch and Retrieval-Augmented AI: Agentic searchUser Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

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.

Problem

• Develops explainable neural inverse kinematics for robotic manipulation
• Compares IKNet variants using SHAP and obstacle avoidance evaluation
• Bridges algorithmic insights with safety for trustworthy AI in robotics

Innovation

• Explainable AI integrates Shapley-value attribution for neural inverse kinematics
• Lightweight IKNet variants with residual connections and decoupling improve performance
• Physics-based simulation evaluates obstacle avoidance and safety with XAI insights