Adaptive and transparent decision-making in autonomous robots through graph-structured world models

Abstract

ABSTRACT The growing ubiquity of autonomous robots across different fields necessitates that agents adapt to diverse tasks and ensure transparency and intelligibility in their decision-making processes. This study presents a novel framework that combines a graph-structured world model with large language models (LLMs) to address these requirements. First, a latent space is created to capture the reachability between distinct states. Next, a graph is constructed within this latent space by clustering an offline dataset that effectively captures the complex dynamics of the environment. Subsequently, LLMs are employed to redefine the reward function and relabel the dataset, thereby establishing a well-defined Markov decision process based on the previously learned graph. This relabeling process ensures that the agent's decision space aligns with user intentions. Consequently, a predictive world model is obtained, offering insights into potential future states and facilitating graph-based planning. Moreover, by inputting the planned path of the agent in the graph-structured world model into LLMs, natural language explanations can be generated to provide transparency in the decision-making process. Experiments on the D4RL benchmark validated the effectiveness of our approach in long-horizon planning, its adaptability to different user tasks, and its inherent explainability.