Causal Correction and Compensation Network for Robotics: Applications and Validation in Continuous Control

Deep Reinforcement Learning (DRL) has achieved remarkable success in robotic control, autonomous driving, and game-playing agents. However, its decision-making process often remains a black box, lacking both interpretability and verifiability. In robotic control tasks, developers cannot pinpoint decision errors or precisely adjust control strategies based solely on observed robot behaviors. To address this challenge, this work proposes an interpretable DRL framework based on a Causal Correction and Compensation Network (C2-Net), which systematically captures the causal relationships underlying decision-making and enhances policy robustness. C2-Net integrates a Graph Neural Network-based Neural Causal Model (GNN-NCM) to compute causal influence weights for each action. These weights are then dynamically applied to correct and compensate the raw policy outputs, thereby balancing performance optimization and transparency. This work validates the approach on OpenAI Gym’s Hopper, Walker2d, and Humanoid environments, as well as the multi-agent AzureLoong platform built on Isaac Gym. In terms of convergence speed, final return, and policy robustness, experimental results show that C2-Net achieves higher performance over both non-causal baselines and conventional attention-based models. Moreover, it provides rich causal explanations for its decisions. The framework represents a principled shift from correlation to causation and offers a practical solution for the safe and reliable deployment of multi-robot systems.