mlsanityData to 14 Oct 2026

Learning Dexterous In-Hand Manipulation

arXiv 1808.00177 ↗First posted 1 Aug 2018cs.LG cs.AI cs.ROcited by 687

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Cited by 135 in Deep reinforcement learning methods, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies which can perform vision-based object reorientation on a physical Shadow Dexterous Hand. The training is performed in a simulated environment in which we randomize many of the physical properties of the system like friction coefficients and an object's appearance. Our policies transfer to the physical robot despite being trained entirely in simulation. Our method does not rely on any human demonstrations, but many behaviors found in human manipulation emerge naturally, including finger gaiting, multi-finger coordination, and the controlled use of gravity. Our results were obtained using the same distributed RL system that was used to train OpenAI Five. We also include a video of our results: https://youtu.be/jwSbzNHGflM

Key facts. Cited by 687 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Aug 2018.

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Citing papers per year: 2019: 10; 2020: 6; 2021: 139; 2022: 126; 2023: 109; 2024: 106; 2025: 90; 2026: 101.

687 papers.

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  1. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 998
  2. 2020MOReL : Model-Based Offline Reinforcement Learningcited by 590
  3. 2024Evaluating Real-World Robot Manipulation Policies in Simulationcited by 495
  4. 2022DayDreamer: World Models for Physical Robot Learningcited by 400
  5. 2021PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-trainingcited by 302
  6. 2021MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learningcited by 290
  7. 2024Diffusion Policy Policy Optimizationcited by 280
  8. 2024A Survey on Vision-Language-Action Models for Embodied AIcited by 279
  9. 2022Masked Visual Pre-training for Motor Controlcited by 248
  10. 2022Real-World Robot Learning with Masked Visual Pre-trainingcited by 245
  11. 2023RVT: Robotic View Transformer for 3D Object Manipulationcited by 225
  12. 2021DexMV: Imitation Learning for Dexterous Manipulation from Human Videoscited by 206
38 more
  1. 2023Real-World Humanoid Locomotion with Reinforcement Learningcited by 187
  2. 2019Imitation Learning via Off-Policy Distribution Matchingcited by 183
  3. 2022A Review of Safe Reinforcement Learning: Methods, Theory and Applicationscited by 181
  4. 2024Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AIcited by 179
  5. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successescited by 176
  6. 2022Evolving Curricula with Regret-Based Environment Designcited by 173
  7. 2021BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environmentscited by 165
  8. 2021A System for General In-Hand Object Re-Orientationcited by 165
  9. 2019Goal-conditioned Imitation Learningcited by 162
  10. 2022Ditto: Building Digital Twins of Articulated Objects from Interactioncited by 156
  11. 2022The Unsurprising Effectiveness of Pre-Trained Vision Models for Controlcited by 155
  12. 2022DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Realitycited by 149
  13. 2023BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objectscited by 148
  14. 2023Structured World Models from Human Videoscited by 147
  15. 2021Tianshou: a Highly Modularized Deep Reinforcement Learning Librarycited by 143
  16. 2021ContactOpt: Optimizing Contact to Improve Graspscited by 139
  17. 2023FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulationcited by 133
  18. 2025EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videoscited by 128
  19. 2023UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policycited by 124
  20. 2020Primal Wasserstein Imitation Learningcited by 121
  21. 2021Learning Generalizable Robotic Reward Functions from "In-The-Wild" Human Videoscited by 119
  22. 2021Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyondcited by 118
  23. 2022In-Hand Object Rotation via Rapid Motor Adaptationcited by 118
  24. 2021Dropout Q-Functions for Doubly Efficient Reinforcement Learningcited by 114
  25. 2021D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactionscited by 113
  26. 2024HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulationcited by 112
  27. 2021Replay-Guided Adversarial Environment Designcited by 111
  28. 2022Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deploymentcited by 108
  29. 2023UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learningcited by 107
  30. 2021B-Pref: Benchmarking Preference-Based Reinforcement Learningcited by 106
  31. 2022Navigating to Objects in the Real Worldcited by 106
  32. 2022Accelerated Policy Learning with Parallel Differentiable Simulationcited by 104
  33. 2022Visual Dexterity: In-Hand Reorientation of Novel and Complex Object Shapescited by 101
  34. 2019Help, Anna! Visual Navigation with Natural Multimodal Assistance via Retrospective Curiosity-Encouraging Imitation Learningcited by 100
  35. 2023General In-Hand Object Rotation with Vision and Touchcited by 99
  36. 2022SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learningcited by 97
  37. 2021Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Interventioncited by 95
  38. 2020State-Only Imitation Learning for Dexterous Manipulationcited by 91

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