Explainable AI for Obstacle-Aware Robotic Manipulation
Published:Dec 29, 2025 09:02
•1 min read
•ArXiv
Analysis
This paper addresses the critical need for explainability in AI-driven robotics, particularly in inverse kinematics (IK). It proposes a methodology to make neural network-based IK models more transparent and safer by integrating Shapley value attribution and physics-based obstacle avoidance evaluation. The study focuses on the ROBOTIS OpenManipulator-X and compares different IKNet variants, providing insights into how architectural choices impact both performance and safety. The work is significant because it moves beyond just improving accuracy and speed of IK and focuses on building trust and reliability, which is crucial for real-world robotic applications.
Key Takeaways
- •Proposes an explainability-centered workflow for neural network-based inverse kinematics.
- •Integrates Shapley value attribution with physics-based obstacle avoidance evaluation.
- •Compares different IKNet variants (Improved IKNet, Focused IKNet).
- •Demonstrates how XAI techniques can improve safety and guide architectural refinements in robotic manipulation.
Reference
“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.”