mlsanityData to 7 Oct 2026

Deep Dynamics Models for Learning Dexterous Manipulation

arXiv 1909.11652 ↗First posted 25 Sep 2019cs.RO cs.LGcited by 200

Not on the map. Why?

Cited by 27 in Offline RL and imitation learning from data, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Dexterous multi-fingered hands can provide robots with the ability to flexibly perform a wide range of manipulation skills. However, many of the more complex behaviors are also notoriously difficult to control: Performing in-hand object manipulation, executing finger gaits to move objects, and exhibiting precise fine motor skills such as writing, all require finely balancing contact forces, breaking and reestablishing contacts repeatedly, and maintaining control of unactuated objects. Learning-based techniques provide the appealing possibility of acquiring these skills directly from data, but current learning approaches either require large amounts of data and produce task-specific policies, or they have not yet been shown to scale up to more complex and realistic tasks requiring fine motor skills. In this work, we demonstrate that our method of online planning with deep dynamics models (PDDM) addresses both of these limitations; we show that improvements in learned dynamics models, together with improvements in online model-predictive control, can indeed enable efficient and effective learning of flexible contact-rich dexterous manipulation skills -- and that too, on a 24-DoF anthropomorphic hand in the real world, using just 4 hours of purely real-world data to learn to simultaneously coordinate multiple free-floating objects. Videos can be found at https://sites.google.com/view/pddm/

Key facts. Cited by 200 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Sep 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 3; 2021: 42; 2022: 40; 2023: 51; 2024: 33; 2025: 20; 2026: 11.

200 papers.

Sort:

By area:

  1. 2025V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planningcited by 738
  2. 2023VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Modelscited by 733
  3. 2021Offline Reinforcement Learning as One Big Sequence Modeling Problemcited by 636
  4. 2020MOPO: Model-based Offline Policy Optimizationcited by 630
  5. 2020MOReL : Model-Based Offline Reinforcement Learningcited by 582
  6. 2022DayDreamer: World Models for Physical Robot Learningcited by 372
  7. 2024DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planningcited by 357
  8. 2024Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllabilitycited by 337
  9. 2022Masked Visual Pre-training for Motor Controlcited by 246
  10. 2021How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learnedcited by 201
  11. 2021A System for General In-Hand Object Re-Orientationcited by 155
  12. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successescited by 153
38 more
  1. 2025Ctrl-World: A Controllable Generative World Model for Robot Manipulationcited by 149
  2. 2023Structured World Models from Human Videoscited by 144
  3. 2022VIOLA: Imitation Learning for Vision-Based Manipulation with Object Proposal Priorscited by 135
  4. 2025Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulationcited by 129
  5. 2021Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skillscited by 125
  6. 2023UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policycited by 117
  7. 2025EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videoscited by 116
  8. 2024SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learningcited by 113
  9. 2022MaskViT: Masked Visual Pre-Training for Video Predictioncited by 112
  10. 20213D Neural Scene Representations for Visuomotor Controlcited by 110
  11. 2025AdaWorld: Learning Adaptable World Models with Latent Actionscited by 108
  12. 2023Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusioncited by 105
  13. 2022In-Hand Object Rotation via Rapid Motor Adaptationcited by 102
  14. 2023UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learningcited by 101
  15. 2023Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysiscited by 99
  16. 2022Visual Dexterity: In-Hand Reorientation of Novel and Complex Object Shapescited by 92
  17. 2020State-Only Imitation Learning for Dexterous Manipulationcited by 90
  18. 2021Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Interventioncited by 90
  19. 2023General In-Hand Object Rotation with Vision and Touchcited by 87
  20. 2022DexVIP: Learning Dexterous Grasping with Human Hand Pose Priors from Videocited by 83
  21. 2022MyoSuite -- A contact-rich simulation suite for musculoskeletal motor controlcited by 80
  22. 2023LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learningcited by 78
  23. 2022Dexterous Imitation Made Easy: A Learning-Based Framework for Efficient Dexterous Manipulationcited by 75
  24. 2021Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapescited by 70
  25. 2022RoboCraft: Learning to See, Simulate, and Shape Elasto-Plastic Objects with Graph Networkscited by 69
  26. 2021Accelerating Robotic Reinforcement Learning via Parameterized Action Primitivescited by 63
  27. 2022Learning Multi-Object Dynamics with Compositional Neural Radiance Fieldscited by 63
  28. 2022See, Hear, and Feel: Smart Sensory Fusion for Robotic Manipulationcited by 61
  29. 2023Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Playcited by 60
  30. 2021Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectificationcited by 59
  31. 2022GenDexGrasp: Generalizable Dexterous Graspingcited by 54
  32. 2021An End-to-End Differentiable Framework for Contact-Aware Robot Designcited by 53
  33. 2022Holo-Dex: Teaching Dexterity with Immersive Mixed Realitycited by 52
  34. 2024AdaptiGraph: Material-Adaptive Graph-Based Neural Dynamics for Robotic Manipulationcited by 52
  35. 2023Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensingcited by 51
  36. 2021Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learningcited by 49
  37. 2022Investigating Compounding Prediction Errors in Learned Dynamics Modelscited by 47
  38. 2022Learning Generalizable Dexterous Manipulation from Human Grasp Affordancecited by 46

50 most cited of 200 All 200 (JSON)