The increasing demand for construction in unstructured and dynamic field conditions requires robots capable of autonomous operation. While imitation learning has shown promise, existing approaches are inherently resource-inefficient and primarily imitate human behaviors rather than underlying decision-making strategies, limiting performance in such conditions. This paper proposes an inverse reinforcement learning-based framework to address these challenges. The framework decomposes construction tasks into subtasks with explicit state features to structure the robot’s decision-making, infers model parameters from limited expert demonstrations, and applies them to the architecture to enable task execution. Simulation-based experimental results demonstrate that the agent trained with the proposed framework achieved a 73% task success rate, outperforming agents trained with traditional approaches. Furthermore, the experiments demonstrated the capacity of IRL to reproduce strategic decision-making of human experts under dynamic and unstructured construction conditions. This study enhances the autonomy of construction robots, further contributing to automation in construction. • A modular inverse reinforcement learning for construction robotics • Decomposes tasks into subtasks and explicit state features in extreme construction • Integrates physical and functional improvements for realistic simulations • Expert demonstrations and parameter estimation to enhance the generalization • Achieves 73% task success rate in dynamic and complex work settings
Kim et al. (Sun,) studied this question.