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Sim2Real Transfer for Reinforcement Learning without Dynamics Randomization

arXiv 2002.11635 ↗First posted 19 Feb 2020cs.AI cs.LG cs.ROcited by 43

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Abstract (arXiv)

In this work we show how to use the Operational Space Control framework (OSC) under joint and cartesian constraints for reinforcement learning in cartesian space. Our method is therefore able to learn fast and with adjustable degrees of freedom, while we are able to transfer policies without additional dynamics randomizations on a KUKA LBR iiwa peg in-hole task. Before learning in simulation starts, we perform a system identification for aligning the simulation environment as far as possible with the dynamics of a real robot. Adding constraints to the OSC controller allows us to learn in a safe way on the real robot or to learn a flexible, goal conditioned policy that can be easily transferred from simulation to the real robot.

Key facts. Cited by 43 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Feb 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 7; 2023: 11; 2024: 11; 2025: 5; 2026: 2.

43 papers.

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  1. 2023MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrationscited by 308
  2. 2024Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AIcited by 175
  3. 2024Flow as the Cross-Domain Manipulation Interfacecited by 126
  4. 2021Robot Learning from Randomized Simulations: A Reviewcited by 56
  5. 2022Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizabilitycited by 32
  6. 2023Polybot: Training One Policy Across Robots While Embracing Variabilitycited by 28
  7. 2024CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulationcited by 25
  8. 2021Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racingcited by 22
  9. 2024Natural Language Can Help Bridge the Sim2Real Gapcited by 21
  10. 2023Large Sequence Models for Sequential Decision-Making: A Surveycited by 19
  11. 2022Transferring Multi-Agent Reinforcement Learning Policies for Autonomous Driving using Sim-to-Realcited by 16
  12. 2025SSRL: Self-Search Reinforcement Learningcited by 13
31 more
  1. 2022Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Taskscited by 12
  2. 2023What do we learn from a large-scale study of pre-trained visual representations in sim and real environments?cited by 11
  3. 2021Procedural Content Generation: Better Benchmarks for Transfer Reinforcement Learningcited by 7
  4. 2024Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systemscited by 6
  5. 2024Graph Reinforcement Learning for Power Grids: A Comprehensive Surveycited by 6
  6. 2025High-Performance Reinforcement Learning on Spot: Optimizing Simulation Parameters with Distributional Measurescited by 5
  7. 2023Pay Attention to How You Drive: Safe and Adaptive Model-Based Reinforcement Learning for Off-Road Drivingcited by 4
  8. 2023A Framework for Few-Shot Policy Transfer through Observation Mapping and Behavior Cloningcited by 4
  9. 2024NatSGD: A Dataset with Speech, Gestures, and Demonstrations for Robot Learning in Natural Human-Robot Interactioncited by 4
  10. 2024Improving Offline Reinforcement Learning with Inaccurate Simulatorscited by 4
  11. 2021Imitation learning for variable speed motion generation over multiple actionscited by 3
  12. 2024Learning Control Barrier Functions and their application in Reinforcement Learning: A Surveycited by 3
  13. 2021Learning Collision-free and Torque-limited Robot Trajectories based on Alternative Safe Behaviorscited by 2
  14. 2022Accelerating Robot Learning of Contact-Rich Manipulations: A Curriculum Learning Studycited by 2
  15. 2022Sim-To-Real Transfer of Visual Grounding for Human-Aided Ambiguity Resolutioncited by 2
  16. 2022A Survey on Deep Reinforcement Learning-based Approaches for Adaptation and Generalizationcited by 1
  17. 2023End-to-End Reinforcement Learning for Torque Based Variable Height Hoppingcited by 1
  18. 2023Resilient Constrained Reinforcement Learningcited by 1
  19. 2024A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Modelcited by 1
  20. 2025Safe Continual Domain Adaptation after Sim2Real Transfer of Reinforcement Learning Policies in Roboticscited by 1
  21. 2021A Quadratic Actor Network for Model-Free Reinforcement Learningcited by 0
  22. 2021Reward Shaping with Subgoals for Social Navigationcited by 0
  23. 2022Concurrent Policy Blending and System Identification for Generalized Assistive Controlcited by 0
  24. 2023Bird's Eye View Based Pretrained World model for Visual Navigationcited by 0
  25. 2023Imitation learning for sim-to-real transfer of robotic cutting policies based on residual Gaussian process disturbance force modelcited by 0
  26. 2023PolyFit: A Peg-in-hole Assembly Framework for Unseen Polygon Shapes via Sim-to-real Adaptationcited by 0
  27. 2024Benchmarks for Reinforcement Learning with Biased Offline Data and Imperfect Simulatorscited by 0
  28. 2025Multi-Robot Collaboration through Reinforcement Learning and Abstract Simulationcited by 0
  29. 2025Towards provable probabilistic safety for scalable embodied AI systemscited by 0
  30. 2026One-Shot Multimodal Learning from Demonstration with Force-Constrained Elastic Mapscited by 0
  31. 2026AI-based worker guidance in assembly and disassembly operations using multimodal ego/exo-centric data capture and structured task knowledgecited by 0