@article{ZHU2026, 
author = {Xiaoqing ZHU and Lanyue BI and Tong WU and Chuan ZHANG and Jiahao WU},
title = {Research on the mechanisms of robotic motion policies based on a neural causal model},
year = {2026},
journal = {Journal of Tsinghua University (Science and Technology)},
volume = {66},
number = {6},
pages = {1153-1163},
keywords = {robot control, explainable reinforcement learning, policy interpretation, graph neural network, causal model},
url = {https://www.sciopen.com/article/10.16511/j.cnki.qhdxxb.2026.26.024},
doi = {10.16511/j.cnki.qhdxxb.2026.26.024},
abstract = {ObjectiveClarifying the causal relationships among states, actions, and rewards in reinforcement learning (RL) for robotic control is crucial for enhancing policy interpretability and for ensuring safe and reliable decision-making. Many RL algorithms still rely on traditional neural network structures and are therefore treated as black boxes that cannot reveal the causal relationships between policy and observation space. Moreover, in high-dimensional and dynamically evolving state-action spaces, conventional attention mechanisms are not effective enough to capture the long-term causal dependencies between state variables and actions. This limitation restricts the explainability of autonomous control systems and poses safety risks when deployed in complex real-world environments.MethodsTherefore, this paper proposed a robotic motion skill interpretation framework based on a graph neural network-neural causal model (GNN-NCM). By replacing attention-based components with GNNs, the model inferred and captured causal influences in sequential decision-making. First, this paper applied conditional independence testing to discover the underlying causal graph and to identify how different state and action variables influenced one another over time. Using the learned causal structure, a GNN was trained to jointly represent nodes (states and actions) and edges (causal dependencies) and to perform both qualitative and quantitative causal inference. The GNN framework integrated structural causal discovery with neural message passing, enabling efficient learning of high-dimensional relationships while preserving interpretability. this paper implemented and validated the algorithm in two representative robotic control environments, LunarLander and Hopper-V4, which differ in control complexity and state dimensionality. this paper used multiple analytical tools, including state decomposition, action separation, and heatmap-based visualization, to assess causal strength and directionality of state-action-reward relationships. This work captured causal weights during decision-making and improved the precision of causal weight prediction, thereby revealing deeper information encoded in the causal model.ResultsExperimental results demonstrated that the proposed GNN-NCM method substantially improved causal inference accuracy, interpretability, and prediction performance relative to conventional attention-based and causal explanation baselines. (1) In the LunarLander environment, the causal prediction error of the GNN inference network decreased by an average of 62%, demonstrating a superior ability to capture stable causal dependencies in continuous control tasks. (2) The model successfully identified state factors that made little contribution to the overall reward while still guiding specific reward components (for example, fuel consumption and landing smoothness). (3) Heatmap visualizations revealed distinct causal interaction patterns among state dimensions, showing, for example, how particular joint angles or velocities causally contributed to reward fluctuations over time. Quantitative evaluation of causal strengths enabled precise attribution of performance outcomes to particular control variables, improving both the interpretability and trustworthiness of learned policies.ConclusionsThe proposed GNN-NCM framework offers a novel, interpretable approach to causal modeling in high-dimensional RL for robot control. By integrating causal structure discovery with neural graph inference, the method narrows the gap between black-box deep RL models and transparent, causality-aware policy representations. It enhances the interpretability, safety, and reliability of decision-making in autonomous robotic systems and demonstrates clear advantages in modeling accuracy and computational efficiency. The results demonstrate that graph-based causal reasoning offers a promising direction for future research in areas such as interpretable RL, interpretable robot control, and safe AI decision-making systems. Further extensions could apply this approach to multi-agent environments and real-world robotic applications, thereby driving the development of reliable and causally based intelligent control frameworks.}
}