mlsanityData to 14 Oct 2026

Grasping in the Wild:Learning 6DoF Closed-Loop Grasping from Low-Cost Demonstrations

arXiv 1912.04344 ↗First posted 9 Dec 2019cs.CV cs.ROcited by 107

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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)

Intelligent manipulation benefits from the capacity to flexibly control an end-effector with high degrees of freedom (DoF) and dynamically react to the environment. However, due to the challenges of collecting effective training data and learning efficiently, most grasping algorithms today are limited to top-down movements and open-loop execution. In this work, we propose a new low-cost hardware interface for collecting grasping demonstrations by people in diverse environments. Leveraging this data, we show that it is possible to train a robust end-to-end 6DoF closed-loop grasping model with reinforcement learning that transfers to real robots. A key aspect of our grasping model is that it uses "action-view" based rendering to simulate future states with respect to different possible actions. By evaluating these states using a learned value function (Q-function), our method is able to better select corresponding actions that maximize total rewards (i.e., grasping success). Our final grasping system is able to achieve reliable 6DoF closed-loop grasping of novel objects across various scene configurations, as well as dynamic scenes with moving objects.

Key facts. Cited by 107 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Dec 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 18; 2022: 24; 2023: 17; 2024: 17; 2025: 22; 2026: 9.

107 papers.

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  1. 2023BridgeData V2: A Dataset for Robot Learning at Scalecited by 657
  2. 2021CLIPort: What and Where Pathways for Robotic Manipulationcited by 622
  3. 2022Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulationcited by 551
  4. 2022VIMA: General Robot Manipulation with Multimodal Promptscited by 407
  5. 2023Affordances from Human Videos as a Versatile Representation for Roboticscited by 226
  6. 2021Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenescited by 218
  7. 2021Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulationcited by 209
  8. 2021DexMV: Imitation Learning for Dexterous Manipulation from Human Videoscited by 206
  9. 2022Human-to-Robot Imitation in the Wildcited by 161
  10. 2024DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulationcited by 160
  11. 2021The Surprising Effectiveness of Representation Learning for Visual Imitationcited by 154
  12. 2024Data Scaling Laws in Imitation Learning for Robotic Manipulationcited by 145
38 more
  1. 2025GraspVLA: a Grasping Foundation Model Pre-trained on Billion-scale Synthetic Action Datacited by 123
  2. 2021Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisationcited by 122
  3. 2021Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objectscited by 119
  4. 2023On Bringing Robots Homecited by 101
  5. 2023GNFactor: Multi-Task Real Robot Learning with Generalizable Neural Feature Fieldscited by 99
  6. 2023Language Embedded Radiance Fields for Zero-Shot Task-Oriented Graspingcited by 91
  7. 2022Touch and Go: Learning from Human-Collected Vision and Touchcited by 87
  8. 2022From One Hand to Multiple Hands: Imitation Learning for Dexterous Manipulation from Single-Camera Teleoperationcited by 82
  9. 2022Dexterous Imitation Made Easy: A Learning-Based Framework for Efficient Dexterous Manipulationcited by 78
  10. 2021UMPNet: Universal Manipulation Policy Network for Articulated Objectscited by 65
  11. 2022MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrationscited by 62
  12. 2023Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Playcited by 62
  13. 2023NeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesiscited by 57
  14. 2023PolarNet: 3D Point Clouds for Language-Guided Robotic Manipulationcited by 56
  15. 2021Learning to See before Learning to Act: Visual Pre-training for Manipulationcited by 51
  16. 2022Learning Generalizable Dexterous Manipulation from Human Grasp Affordancecited by 51
  17. 2023Zero-Shot Robot Manipulation from Passive Human Videoscited by 51
  18. 2022Vision-Based Manipulators Need to Also See from Their Handscited by 47
  19. 2022Graph Inverse Reinforcement Learning from Diverse Videoscited by 46
  20. 2024HRP: Human Affordances for Robotic Pre-Trainingcited by 44
  21. 2024Robot Utility Models: General Policies for Zero-Shot Deployment in New Environmentscited by 44
  22. 2025Emergence of Human to Robot Transfer in Vision-Language-Action Modelscited by 42
  23. 2023M2T2: Multi-Task Masked Transformer for Object-centric Pick and Placecited by 41
  24. 2025DexWild: Dexterous Human Interactions for In-the-Wild Robot Policiescited by 38
  25. 2024ForceMimic: Force-Centric Imitation Learning with Force-Motion Capture System for Contact-Rich Manipulationcited by 36
  26. 2024Diffusion Meets DAgger: Supercharging Eye-in-hand Imitation Learningcited by 35
  27. 2023Learning Human-to-Robot Handovers from Point Cloudscited by 34
  28. 2025FACTR: Force-Attending Curriculum Training for Contact-Rich Policy Learningcited by 34
  29. 2023Grasp-Anything: Large-scale Grasp Dataset from Foundation Modelscited by 33
  30. 2025BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activitiescited by 31
  31. 2022Learning to Grasp the Ungraspable with Emergent Extrinsic Dexteritycited by 28
  32. 2023AR2-D2:Training a Robot Without a Robotcited by 27
  33. 2025RoboCopilot: Human-in-the-loop Interactive Imitation Learning for Robot Manipulationcited by 26
  34. 2024Bimanual Dexterity for Complex Taskscited by 25
  35. 2021Generalization Through Hand-Eye Coordination: An Action Space for Learning Spatially-Invariant Visuomotor Controlcited by 24
  36. 2022Learning High-DOF Reaching-and-Grasping via Dynamic Representation of Gripper-Object Interactioncited by 24
  37. 2022SEIL: Simulation-augmented Equivariant Imitation Learningcited by 23
  38. 2024DNAct: Diffusion Guided Multi-Task 3D Policy Learningcited by 23

50 most cited of 107 All 107 (JSON)