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

• URL: http://arxiv.org/abs/2512.23312v1
• Date: Mon, 29 Dec 2025 09:02:02 GMT
• Title: Explainable Neural Inverse Kinematics for Obstacle-Aware Robotic Manipulation: A Comparative Analysis of IKNet Variants
• Authors: Sheng-Kai Chen, Yi-Ling Tsai, Chun-Chih Chang, Yan-Chen Chen, Po-Chiang Lin,
• Abstract summary: Deep neural networks have accelerated inverse-kinematics (IK) inference to the point where low cost manipulators can execute complex trajectories in real time.<n>This study proposes an explainability centered workflow that integrates Shapley-value attribution with physics-based obstacle avoidance evaluation.
• Score: 0.28544513613730205
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• IoUCert: Robustness Verification for Anchor-based Object Detectors [58.35703549470485]
  We introduce IoUCert, a novel formal verification framework designed specifically to overcome these bottlenecks in anchor-based object detection architectures.<n>We show that our method enables the robustness verification of realistic, anchor-based models including SSD, YOLOv2, and YOLOv3 variants against various input perturbations.
  arXiv  Detail & Related papers  (2026-03-03T14:36:46Z)
• Towards Worst-Case Guarantees with Scale-Aware Interpretability [58.519943565092724]
  Neural networks organize information according to the hierarchical, multi-scale structure of natural data.<n>We propose a unifying research agenda -- emphscale-aware interpretability -- to develop formal machinery and interpretability tools.
  arXiv  Detail & Related papers  (2026-02-05T01:22:31Z)
• Interpretable Hybrid Deep Q-Learning Framework for IoT-Based Food Spoilage Prediction with Synthetic Data Generation and Hardware Validation [0.5417521241272645]
  The need for an intelligent, real-time spoilage prediction system has become critical in modern IoT-driven food supply chains.<n>We propose a hybrid reinforcement learning framework integrating Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for enhanced spoilage prediction.
  arXiv  Detail & Related papers  (2025-12-22T12:59:48Z)
• Knowledge-Informed Neural Network for Complex-Valued SAR Image Recognition [51.03674130115878]
  We introduce the Knowledge-Informed Neural Network (KINN), a lightweight framework built upon a novel "compression-aggregation-compression" architecture.<n>KINN establishes a state-of-the-art in parameter-efficient recognition, offering exceptional generalization in data-scarce and out-of-distribution scenarios.
  arXiv  Detail & Related papers  (2025-10-23T07:12:26Z)
• Bridging Semantics & Structure for Software Vulnerability Detection using Hybrid Network Models [0.0]
  We capture control- and data-flow relations as complex interaction networks.<n>Our framework combines graph representations with light-weight (4B) local LLMs.<n>Our method achieves 93.57% accuracy-an 8.36% gain over Graph Attention Network-based embeddings.
  arXiv  Detail & Related papers  (2025-10-11T19:32:00Z)
• A Pseudo Global Fusion Paradigm-Based Cross-View Network for LiDAR-Based Place Recognition [12.93382945887946]
  LiDAR-based Place Recognition (LPR) remains a critical task in Embodied Artificial Intelligence (AI) and Autonomous Driving.<n>Existing approaches reduce place recognition to a Euclidean distance-based metric learning task, neglecting the feature space's intrinsic structures and intra-class variances.<n>We propose a novel cross-view network based on an innovative fusion paradigm to address these challenges.
  arXiv  Detail & Related papers  (2025-08-12T13:12:48Z)
• Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning [54.26807397329468]
  This work explores a previously overlooked vulnerability in distributed deep learning systems.<n>An adversary who intercepts the intermediate features transmitted between them can still pose a serious threat.<n>We propose an exploitation strategy specifically designed for distributed settings.
  arXiv  Detail & Related papers  (2025-07-09T20:09:00Z)
• Assured Autonomy with Neuro-Symbolic Perception [11.246557832016238]
  Many state-of-the-art AI models deployed in cyber-physical systems (CPS) are pattern-matchers.<n>With limited security guarantees, there are concerns for their reliability in safety-critical and contested domains.<n>We propose a paradigm shift that imbues data-driven perception models with symbolic structure.
  arXiv  Detail & Related papers  (2025-05-27T15:21:06Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• LoRA-Ensemble: Efficient Uncertainty Modelling for Self-Attention Networks [52.46420522934253]
  We introduce LoRA-Ensemble, a parameter-efficient ensembling method for self-attention networks.<n>The method not only outperforms state-of-the-art implicit techniques like BatchEnsemble, but even matches or exceeds the accuracy of an Explicit Ensemble.
  arXiv  Detail & Related papers  (2024-05-23T11:10:32Z)
• Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance [95.86944752753564]
  We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
  arXiv  Detail & Related papers  (2020-07-28T07:34:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.