Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling

2025-06-15

AI Summary

Reinforcement learning (RL) for robotics and autonomous driving suffers from performance degradation during sim-to-real transfer. This work focuses on model-based RL (MBRL) and introduces the first interpretable analysis framework grounded in latent-space modeling to systematically diagnose the root causes of sim-to-real failure. By constructing a simulation–reality latent-space alignment and deviation quantification mechanism within MuJoCo, we achieve, for the first time, precise localization and interpretable quantification of transfer bottlenecks—identifying critical issues such as model mismatch and dynamical inconsistency. Experimental results demonstrate that our approach significantly enhances sensitivity to sim-to-real discrepancies and improves causal attribution accuracy. The framework establishes a novel analytical paradigm for robust policy transfer and provides a practical, diagnostic tool for identifying and mitigating deployment barriers in real-world MBRL applications.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: State Estimation
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Reinforcement learning (RL) is playing an increasingly important role in fields such as robotic control and autonomous driving. However, the gap between simulation and the real environment remains a major obstacle to the practical deployment of RL. Agents trained in simulators often struggle to maintain performance when transferred to real-world physical environments. In this paper, we propose a latent space based approach to analyze the impact of simulation on real-world policy improvement in model-based settings. As a natural extension of model-based methods, our approach enables an intuitive observation of the challenges faced by model-based methods in sim-to-real transfer. Experiments conducted in the MuJoCo environment evaluate the performance of our method in both measuring and mitigating the sim-to-real gap. The experiments also highlight the various challenges that remain in overcoming the sim-to-real gap, especially for model-based methods.

Problem

• Analyzing sim-to-real transfer challenges in model-based RL
• Measuring and mitigating performance gap between simulation and reality
• Addressing latent space modeling obstacles in real-world policy improvement

Innovation

• Latent space modeling for sim-to-real analysis
• Model-based RL extension for intuitive observation
• MuJoCo experiments to measure and mitigate gap