mlsanityData to 7 Oct 2026

Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey

arXiv 2009.13303 ↗First posted 24 Sep 2020cs.LG cs.ROcited by 388

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Cited by 26 in Offline RL and imitation learning from data, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Deep reinforcement learning has recently seen huge success across multiple areas in the robotics domain. Owing to the limitations of gathering real-world data, i.e., sample inefficiency and the cost of collecting it, simulation environments are utilized for training the different agents. This not only aids in providing a potentially infinite data source, but also alleviates safety concerns with real robots. Nonetheless, the gap between the simulated and real worlds degrades the performance of the policies once the models are transferred into real robots. Multiple research efforts are therefore now being directed towards closing this sim-to-real gap and accomplish more efficient policy transfer. Recent years have seen the emergence of multiple methods applicable to different domains, but there is a lack, to the best of our knowledge, of a comprehensive review summarizing and putting into context the different methods. In this survey paper, we cover the fundamental background behind sim-to-real transfer in deep reinforcement learning and overview the main methods being utilized at the moment: domain randomization, domain adaptation, imitation learning, meta-learning and knowledge distillation. We categorize some of the most relevant recent works, and outline the main application scenarios. Finally, we discuss the main opportunities and challenges of the different approaches and point to the most promising directions.

Key facts. Cited by 388 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Sep 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 23; 2022: 57; 2023: 77; 2024: 75; 2025: 84; 2026: 72.

388 papers.

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  1. 2023Learning Interactive Real-World Simulatorscited by 440
  2. 2021A Survey of Zero-shot Generalisation in Deep Reinforcement Learningcited by 164
  3. 2022SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learningcited by 93
  4. 2024What Matters in Building Vision-Language-Action Models for Generalist Robotscited by 88
  5. 2025WorldGym: World Model as An Environment for Policy Evaluationcited by 61
  6. 2022Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environmentscited by 54
  7. 2024FLaRe: Achieving Masterful and Adaptive Robot Policies with Large-Scale Reinforcement Learning Fine-Tuningcited by 54
  8. 2023Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coveragecited by 51
  9. 2024RoboGSim: A Real2Sim2Real Robotic Gaussian Splatting Simulatorcited by 50
  10. 2024GenSim2: Scaling Robot Data Generation with Multi-modal and Reasoning LLMscited by 48
  11. 2026Mind the Sim2Real Gap in User Simulation for Agentic Taskscited by 44
  12. 2024VLATest: Testing and Evaluating Vision-Language-Action Models for Robotic Manipulationcited by 43
38 more
  1. 2024PoCo: Policy Composition from and for Heterogeneous Robot Learningcited by 42
  2. 2025Residual Off-Policy RL for Finetuning Behavior Cloning Policiescited by 42
  3. 2024Towards Generalist Robot Learning from Internet Video: A Surveycited by 40
  4. 2024BadRobot: Jailbreaking Embodied LLM Agents in the Physical Worldcited by 40
  5. 2023Finetuning Offline World Models in the Real Worldcited by 39
  6. 2023Knowledge Distillation on Graphs: A Surveycited by 38
  7. 2023Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learningcited by 36
  8. 2025A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Modelscited by 36
  9. 2021From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligencecited by 35
  10. 2024Humanoid-Gym: Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real Transfercited by 35
  11. 2025World4RL: Diffusion World Models for Policy Refinement with Reinforcement Learning for Robotic Manipulationcited by 35
  12. 2024Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applicationscited by 34
  13. 2022Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizabilitycited by 32
  14. 2022Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarkingcited by 31
  15. 2023Probabilistic Adaptation of Text-to-Video Modelscited by 29
  16. 2022Towards Safe Reinforcement Learning with a Safety Editor Policycited by 28
  17. 2022Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workerscited by 28
  18. 2021Towards Theoretical Understandings of Robust Markov Decision Processes: Sample Complexity and Asymptoticscited by 27
  19. 2022Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Trainingcited by 26
  20. 2024ReVLA: Reverting Visual Domain Limitation of Robotic Foundation Modelscited by 26
  21. 2024Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximationcited by 25
  22. 2026World-Gymnast: Training Robots with Reinforcement Learning in a World Modelcited by 25
  23. 2023Evolutionary Reinforcement Learning: A Surveycited by 24
  24. 2024Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithmscited by 24
  25. 2022Provably Safe Reinforcement Learning via Action Projection using Reachability Analysis and Polynomial Zonotopescited by 23
  26. 2023Markerless Camera-to-Robot Pose Estimation via Self-supervised Sim-to-Real Transfercited by 23
  27. 2022Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 Diabetescited by 22
  28. 2025ReBot: Scaling Robot Learning with Real-to-Sim-to-Real Robotic Video Synthesiscited by 22
  29. 2023Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directionscited by 20
  30. 2023On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfercited by 20
  31. 2024MENTOR: Mixture-of-Experts Network with Task-Oriented Perturbation for Visual Reinforcement Learningcited by 20
  32. 2021A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directionscited by 19
  33. 2023Automated Cyber Defence: A Reviewcited by 19
  34. 2023Learning to Extrapolate: A Transductive Approachcited by 19
  35. 2025Refined Policy Distillation: From VLA Generalists to RL Expertscited by 19
  36. 2023Dynamics Generalisation in Reinforcement Learning via Adaptive Context-Aware Policiescited by 18
  37. 2023TWIST: Teacher-Student World Model Distillation for Efficient Sim-to-Real Transfercited by 18
  38. 2024One-Shot Imitation Learning with Invariance Matching for Robotic Manipulationcited by 18

50 most cited of 388 All 388 (JSON)

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  1. 2016Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model
  2. 2025An Introduction to Deep Reinforcement and Imitation Learning
  3. 2025X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real
  4. 2026NavRL++: A System-Level Framework for Improving Sim-to-Real Transfer in Reinforcement Learning-Based Robot Navigation
  5. 2024Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrations
  6. 2026WorldSample: Closed-loop Real-robot RL with World Modelling
  7. 2025Steering Your Diffusion Policy with Latent Space Reinforcement Learning
  8. 2026Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation