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

Source: arXiv - 2512.23312v1

Overview

This paper tackles a practical problem for anyone building low‑cost robotic arms: how to compute joint commands (inverse kinematics, or IK) fast enough for real‑time control and keep the decision process transparent enough to satisfy emerging safety and responsible‑AI regulations. By marrying a lightweight neural‑IK model with SHAP‑based explainability and physics‑driven collision checks, the authors show a path toward trustworthy, obstacle‑aware manipulation on the popular ROBOTIS OpenManipulator‑X platform.

Key Contributions

• Two streamlined IKNet variants – Improved IKNet (adds residual connections) and Focused IKNet (decouples position and orientation) – that retain the speed of the original model while reducing parameters.
• Explainability pipeline that couples SHAP (Shapley‑value) attributions with the InterpretML toolkit to expose how each Cartesian pose component influences predicted joint angles.
• Safety‑centric evaluation using a physics simulator: random single‑ and multi‑obstacle scenes, capsule‑based collision detection, and trajectory‑level metrics (clearance, path length, positional error).
• Empirical link between attribution balance (how evenly importance is spread across pose dimensions) and physical safety margins, revealing hidden failure modes in otherwise accurate IK predictions.
• Guidelines for developers on how XAI insights can drive architecture tweaks and deployment strategies for obstacle‑aware robot manipulation.

Methodology

1. Data Generation – A synthetic dataset of millions of pose‑joint pairs is created by sampling the robot’s reachable workspace and solving the IK analytically for ground‑truth joint angles.
2. Model Variants –
   • Improved IKNet: inserts residual shortcuts into the original fully‑connected layers to ease gradient flow.
   • Focused IKNet: splits the network into two branches, one handling Cartesian position (x, y, z) and the other handling orientation (roll, pitch, yaw), then merges the outputs.
3. Explainability – After training, SHAP values are computed for each input dimension per prediction. Global importance rankings (averaged over the test set) and local heat‑maps (per‑sample) are visualized with Partial Dependence Plots (PDPs) via InterpretML.
4. Simulation & Safety Testbed – Each model is deployed in a Gazebo‑style simulator where the arm follows a series of target poses while random obstacles (cylinders, boxes) are placed. Forward kinematics converts predicted joints back to end‑effector poses; capsule‑based collision checks flag any penetrations. Metrics recorded:
   • Positional RMSE (accuracy)
   • Minimum clearance to obstacles (safety)
   • Trajectory smoothness (joint‑space jerk)
5. Analysis – Correlate SHAP‑derived attribution balance with the safety metrics to identify which architectural choices lead to more robust, obstacle‑aware behavior.

Results & Findings

• Attribution balance matters: Models that spread SHAP importance more evenly across all six pose dimensions (especially the Focused IKNet) consistently achieved larger safety margins.
• Residual connections improve gradient flow, yielding a modest accuracy boost without extra cost.
• Decoupling position/orientation reduces non‑linear coupling errors, which translates into smoother joint trajectories and fewer collision incidents.
• Heat‑maps reveal specific failure modes—e.g., when the orientation dimensions dominate the attribution, the arm tends to “twist” into obstacles despite accurate positioning.

Practical Implications

• Deployable on commodity hardware – The sub‑40 µs inference time means the models can run on micro‑controllers or edge GPUs typical in hobbyist and small‑scale industrial robots.
• Safety certification aid – SHAP visualizations provide auditors with concrete evidence of why a joint command was chosen, supporting compliance with upcoming responsible‑AI standards for robotics.
• Rapid prototyping – Developers can generate synthetic pose‑joint data for any manipulator, train the lightweight variants, and immediately obtain XAI diagnostics to spot risky configurations before field trials.
• Obstacle‑aware motion planning – By integrating the attribution‑clearance correlation into a higher‑level planner, a system can preferentially select IK solutions that are both accurate and “explainably safe,” reducing the need for expensive runtime collision checking.
• Model debugging – When a robot misbehaves, the per‑sample SHAP heat‑maps pinpoint which input dimensions the network over‑relied on, guiding targeted data augmentation or architecture tweaks.

Limitations & Future Work

• Synthetic data bias – The training set is entirely simulated; real‑world sensor noise and unmodeled dynamics could degrade performance.
• Single‑arm focus – Experiments are limited to the OpenManipulator‑X; scaling to higher‑DOF arms or dual‑arm setups may expose new coupling challenges.
• Static obstacles only – Dynamic obstacle scenarios (moving humans, tools) were not evaluated; extending the pipeline to incorporate temporal SHAP explanations is an open direction.
• Explainability overhead – Computing SHAP values for every inference is costly; future work should explore lightweight attribution approximations suitable for on‑board diagnostics.

Overall, the study demonstrates that a blend of lightweight neural IK, rigorous XAI, and physics‑based safety testing can deliver fast, trustworthy manipulation—an encouraging blueprint for developers aiming to bring intelligent robots into real‑world, safety‑critical applications.

Authors

• Sheng‑Kai Chen
• Yi‑Ling Tsai
• Chun‑Chih Chang
• Yan‑Chen Chen
• Po‑Chiang Lin

Paper Information

• arXiv ID: 2512.23312v1
• Categories: cs.RO, cs.AI
• Published: December 29, 2025
• PDF: Download PDF