mlsanityData to 7 Oct 2026

Flow-based Domain Randomization for Learning and Sequencing Robotic Skills

arXiv 2502.01800 ↗First posted 3 Feb 2025cs.RO cs.AI cs.LGcited by 4

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Abstract (arXiv)

Domain randomization in reinforcement learning is an established technique for increasing the robustness of control policies trained in simulation. By randomizing environment properties during training, the learned policy can become robust to uncertainties along the randomized dimensions. While the environment distribution is typically specified by hand, in this paper we investigate automatically discovering a sampling distribution via entropy-regularized reward maximization of a normalizing-flow-based neural sampling distribution. We show that this architecture is more flexible and provides greater robustness than existing approaches that learn simpler, parameterized sampling distributions, as demonstrated in six simulated and one real-world robotics domain. Lastly, we explore how these learned sampling distributions, combined with a privileged value function, can be used for out-of-distribution detection in an uncertainty-aware multi-step manipulation planner.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 37 of its 48 references that are in the map. First posted on arXiv 3 Feb 2025.

Builds on

37 of 48 references.

References · 37

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2015Variational Inference with Normalizing Flowscited by 2,161
  4. 2019Solving Rubik's Cube with a Robot Handcited by 699
  5. 2017Sim-to-Real Transfer of Robotic Control with Dynamics Randomizationcited by 616
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  7. 2023FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objectscited by 411
  8. 2023Orbit: A Unified Simulation Framework for Interactive Robot Learning Environmentscited by 239
  9. 2018Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experiencecited by 203
  10. 2017Asymmetric Actor Critic for Image-Based Robot Learningcited by 202
  11. 2022MegaPose: 6D Pose Estimation of Novel Objects via Render & Comparecited by 198
  12. 2019Active Domain Randomizationcited by 161
25 more
  1. 2018Assessing Generalization in Deep Reinforcement Learningcited by 132
  2. 2020The Ingredients of Real-World Robotic Reinforcement Learningcited by 111
  3. 2023Generative Skill Chaining: Long-Horizon Skill Planning with Diffusion Modelscited by 87
  4. 2019BayesSim: adaptive domain randomization via probabilistic inference for robotics simulatorscited by 78
  5. 2021Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Taskscited by 75
  6. 2021RL for Latent MDPs: Regret Guarantees and a Lower Boundcited by 69
  7. 2021Understanding Domain Randomization for Sim-to-real Transfercited by 69
  8. 2019Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewardscited by 58
  9. 2023IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Realitycited by 44
  10. 2018Policy Transfer with Strategy Optimizationcited by 41
  11. 2019Assessing Transferability from Simulation to Reality for Reinforcement Learningcited by 29
  12. 2020A Modular Robotic Arm Control Stack for Research: Franka-Interface and FrankaPycited by 24
  13. 2021A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learningcited by 21
  14. 2020Crossing The Gap: A Deep Dive into Zero-Shot Sim-to-Real Transfer for Dynamicscited by 20
  15. 2024FORGE: Force-Guided Exploration for Robust Contact-Rich Manipulation under Uncertaintycited by 18
  16. 2024Partially Observable Task and Motion Planning with Uncertainty and Risk Awarenesscited by 17
  17. 2023Domain Randomization via Entropy Maximizationcited by 15
  18. 2022Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Taskscited by 12
  19. 2024Robust Fast Adaptation from Adversarially Explicit Task Distribution Generationcited by 12
  20. 2020Learning Active Task-Oriented Exploration Policies for Bridging the Sim-to-Real Gapcited by 11
  21. 2022Gradual Domain Adaptation via Normalizing Flowscited by 11
  22. 2023Bridging the Sim-to-Real Gap with Dynamic Compliance Tuning for Industrial Insertioncited by 8
  23. 2022Task-Directed Exploration in Continuous POMDPs for Robotic Manipulation of Articulated Objectscited by 7
  24. 2020A Learning Approach to Robot-Agnostic Force-Guided High Precision Assemblycited by 6
  25. 2023Bayes3D: fast learning and inference in structured generative models of 3D objects and scenescited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Dreaming the Unseen: World Model-regularized Diffusion Policy for Out-of-Distribution Robustnesscited by 2
  2. 2026Online Adaptive Reinforcement Learning with Echo State Networks for Non-Stationary Dynamicscited by 0
  3. 2026DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learningcited by 0
  4. 2026Generalizing HVAC Control With Domain Randomized Reinforcement Learningcited by 0