Explainable AI Boosts Safety for Real-Time Robotic Inverse Kinematics

Global: Explainable AI Boosts Safety for Real-Time Robotic Inverse Kinematics

Researchers presented a workflow that combines Shapley-value attribution with physics‑based obstacle avoidance to improve the transparency and safety of inverse‑kinematics (IK) inference for the ROBOTIS OpenManipulator‑X. The study, posted on arXiv in December 2025, addresses emerging responsible‑AI regulations that demand explainability in low‑cost manipulators capable of executing complex trajectories in real time.

Background on Real-Time Inverse Kinematics

Deep neural networks have accelerated IK computation, enabling inexpensive robotic arms to plan and follow intricate motion paths without noticeable latency. However, the black‑box nature of these models conflicts with safety standards that require clear insight into decision‑making processes, especially when robots operate near humans or obstacles.

Explainability‑Centered Workflow

The proposed methodology integrates the SHAP (Shapley Additive exPlanations) framework with a physics‑driven obstacle‑avoidance evaluator. SHAP generates both global and local importance rankings for input pose dimensions, while the InterpretML toolkit visualizes partial‑dependence relationships that reveal nonlinear couplings between Cartesian targets and joint angles.

Network Variants and Training

Building on the original IKNet architecture, two lightweight models were created: Improved IKNet, which adds residual connections, and Focused IKNet, which decouples position and orientation processing. Both were trained on a large synthetically generated dataset of pose‑joint pairs, ensuring coverage of diverse configurations.

Simulation‑Based Safety Assessment

Each network was embedded in a simulator that presented randomized single‑ and multi‑obstacle scenes. Forward‑kinematics calculations, capsule‑based collision checks, and trajectory‑metric analyses quantified how attribution balance correlated with physical clearance from obstacles.

Key Findings on Attribution and Clearance

Heat‑map visualizations indicated that models distributing importance more evenly across pose dimensions tended to preserve wider safety margins while maintaining positional accuracy. Conversely, networks that concentrated attribution on a few dimensions exhibited tighter clearances and higher collision risk.

Implications for Trustworthy Robotics

The results demonstrate that XAI techniques can expose hidden failure modes, guide architectural refinements, and support obstacle‑aware deployment strategies for learning‑based IK. By aligning model interpretability with safety metrics, the workflow offers a concrete path toward responsible‑AI compliant manipulation in real‑world settings.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.