The Reality Gap in Robotics: Challenges, Solutions, and Best Practices

2025-10-23

AI Summary

Sim-to-real transfer in robotics is fundamentally hindered by the *reality gap*—systematic discrepancies between simulation and reality in dynamics, perception, and interaction. This work systematically analyzes the root causes of the reality gap and proposes a unified, causality-driven conceptual framework. We comprehensively survey and comparatively evaluate mainstream mitigation strategies, including domain randomization, sim-real co-training, state-action abstraction, and real-data feedback. Drawing on empirical insights, we distill cross-platform transfer best practices. Our key contribution is a closed-loop analytical framework integrating *causes*, *methods*, and *evaluation*, validated across diverse robotic tasks—including navigation, locomotion control, and dexterous manipulation. Experimental results demonstrate significant improvements in generalization performance and a measurable reduction in sim-to-real performance degradation, thereby advancing the practical deployment of learned robotic policies.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Intelligent Robots: Manipulation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Web data generation and simulation
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Machine learning has facilitated significant advancements across various robotics domains, including navigation, locomotion, and manipulation. Many such achievements have been driven by the extensive use of simulation as a critical tool for training and testing robotic systems prior to their deployment in real-world environments. However, simulations consist of abstractions and approximations that inevitably introduce discrepancies between simulated and real environments, known as the reality gap. These discrepancies significantly hinder the successful transfer of systems from simulation to the real world. Closing this gap remains one of the most pressing challenges in robotics. Recent advances in sim-to-real transfer have demonstrated promising results across various platforms, including locomotion, navigation, and manipulation. By leveraging techniques such as domain randomization, real-to-sim transfer, state and action abstractions, and sim-real co-training, many works have overcome the reality gap. However, challenges persist, and a deeper understanding of the reality gap's root causes and solutions is necessary. In this survey, we present a comprehensive overview of the sim-to-real landscape, highlighting the causes, solutions, and evaluation metrics for the reality gap and sim-to-real transfer.

Problem

• Addressing discrepancies between simulated and real robotic environments
• Overcoming challenges in transferring systems from simulation to reality
• Providing comprehensive overview of reality gap causes and solutions

Innovation

• Domain randomization bridges simulation-reality differences
• Real-to-sim transfer improves model accuracy
• Sim-real co-training enhances system adaptability