mlsanityData to 7 Oct 2026

Visual Imitation Made Easy

arXiv 2008.04899 ↗First posted 11 Aug 2020cs.RO cs.CV cs.LGcited by 111

On the map: Offline RL and imitation learning from data → Robot manipulation imitation learning

Abstract (arXiv)

Visual imitation learning provides a framework for learning complex manipulation behaviors by leveraging human demonstrations. However, current interfaces for imitation such as kinesthetic teaching or teleoperation prohibitively restrict our ability to efficiently collect large-scale data in the wild. Obtaining such diverse demonstration data is paramount for the generalization of learned skills to novel scenarios. In this work, we present an alternate interface for imitation that simplifies the data collection process while allowing for easy transfer to robots. We use commercially available reacher-grabber assistive tools both as a data collection device and as the robot's end-effector. To extract action information from these visual demonstrations, we use off-the-shelf Structure from Motion (SfM) techniques in addition to training a finger detection network. We experimentally evaluate on two challenging tasks: non-prehensile pushing and prehensile stacking, with 1000 diverse demonstrations for each task. For both tasks, we use standard behavior cloning to learn executable policies from the previously collected offline demonstrations. To improve learning performance, we employ a variety of data augmentations and provide an extensive analysis of its effects. Finally, we demonstrate the utility of our interface by evaluating on real robotic scenarios with previously unseen objects and achieve a 87% success rate on pushing and a 62% success rate on stacking. Robot videos are available at https://dhiraj100892.github.io/Visual-Imitation-Made-Easy.

Key facts. Cited by 111 papers in the map (data through 7 Oct 2026). Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data. First posted on arXiv 11 Aug 2020.

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Citing papers per year: 2021: 16; 2022: 19; 2023: 25; 2024: 22; 2025: 22; 2026: 7.

111 papers.

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  1. 2023RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Controlcited by 2,993
  2. 2021What Matters in Learning from Offline Human Demonstrations for Robot Manipulationcited by 699
  3. 2022R3M: A Universal Visual Representation for Robot Manipulationcited by 678
  4. 2023BridgeData V2: A Dataset for Robot Learning at Scalecited by 634
  5. 2022BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learningcited by 517
  6. 2021Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learningcited by 359
  7. 2023MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrationscited by 308
  8. 2021Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasetscited by 248
  9. 2023Affordances from Human Videos as a Versatile Representation for Roboticscited by 221
  10. 2021DexMV: Imitation Learning for Dexterous Manipulation from Human Videoscited by 194
  11. 2021Reinforcement Learning with Prototypical Representationscited by 179
  12. 2023RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shotcited by 173
38 more
  1. 2024HumanPlus: Humanoid Shadowing and Imitation from Humanscited by 164
  2. 2024EgoMimic: Scaling Imitation Learning via Egocentric Videocited by 160
  3. 2022Human-to-Robot Imitation in the Wildcited by 157
  4. 2023Open-World Object Manipulation using Pre-trained Vision-Language Modelscited by 155
  5. 2021The Surprising Effectiveness of Representation Learning for Visual Imitationcited by 149
  6. 2024DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulationcited by 149
  7. 2024Data Scaling Laws in Imitation Learning for Robotic Manipulationcited by 144
  8. 2024Gen2Act: Human Video Generation in Novel Scenarios enables Generalizable Robot Manipulationcited by 142
  9. 2026π0.7: a Steerable Generalist Robotic Foundation Model with Emergent Capabilitiescited by 127
  10. 2021Learning Generalizable Robotic Reward Functions from "In-The-Wild" Human Videoscited by 116
  11. 2024Track2Act: Predicting Point Tracks from Internet Videos enables Generalizable Robot Manipulationcited by 116
  12. 2024DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learningcited by 104
  13. 2021Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentationcited by 101
  14. 2022VideoDex: Learning Dexterity from Internet Videoscited by 101
  15. 2023On Bringing Robots Homecited by 101
  16. 2021Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstrationcited by 92
  17. 2022From One Hand to Multiple Hands: Imitation Learning for Dexterous Manipulation from Single-Camera Teleoperationcited by 79
  18. 2022Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trialscited by 78
  19. 2023Decomposing the Generalization Gap in Imitation Learning for Visual Robotic Manipulationcited by 78
  20. 2022Dexterous Imitation Made Easy: A Learning-Based Framework for Efficient Dexterous Manipulationcited by 75
  21. 2023Teach a Robot to FISH: Versatile Imitation from One Minute of Demonstrationscited by 74
  22. 2022CACTI: A Framework for Scalable Multi-Task Multi-Scene Visual Imitation Learningcited by 70
  23. 2021A Workflow for Offline Model-Free Robotic Reinforcement Learningcited by 68
  24. 2022Watch and Match: Supercharging Imitation with Regularized Optimal Transportcited by 64
  25. 2021Visual Semantic Role Labeling for Video Understandingcited by 60
  26. 2023Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Playcited by 60
  27. 2022MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrationscited by 59
  28. 2023Imitation Bootstrapped Reinforcement Learningcited by 54
  29. 2022Holo-Dex: Teaching Dexterity with Immersive Mixed Realitycited by 52
  30. 2023Towards Generalizable Zero-Shot Manipulation via Translating Human Interaction Planscited by 51
  31. 2023Zero-Shot Robot Manipulation from Passive Human Videoscited by 49
  32. 2021Towards More Generalizable One-shot Visual Imitation Learningcited by 48
  33. 2024SkillMimicGen: Automated Demonstration Generation for Efficient Skill Learning and Deploymentcited by 48
  34. 2022Learning Generalizable Dexterous Manipulation from Human Grasp Affordancecited by 46
  35. 2022Graph Inverse Reinforcement Learning from Diverse Videoscited by 45
  36. 2024Re-Mix: Optimizing Data Mixtures for Large Scale Imitation Learningcited by 44
  37. 2024Robot Utility Models: General Policies for Zero-Shot Deployment in New Environmentscited by 43
  38. 2024ForceMimic: Force-Centric Imitation Learning with Force-Motion Capture System for Contact-Rich Manipulationcited by 36

50 most cited of 111 All 111 (JSON)

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  1. 2024VITAL: Interactive Few-Shot Imitation Learning via Visual Human-in-the-Loop Corrections
  2. 2026Human-to-Robot Interaction: Learning from Video Demonstration for Robot Imitation
  3. 2024ReMoBot: Retrieval-Based Few-Shot Imitation Learning for Mobile Manipulation with Vision Foundation Models
  4. 2021Manipulator-Independent Representations for Visual Imitation
  5. 2021Seeing All the Angles: Learning Multiview Manipulation Policies for Contact-Rich Tasks from Demonstrations
  6. 2022Eliciting Compatible Demonstrations for Multi-Human Imitation Learning
  7. 2025Bootstrapping Imitation Learning for Long-horizon Manipulation via Hierarchical Data Collection Space
  8. 2022Evaluating the Effectiveness of Corrective Demonstrations and a Low-Cost Sensor for Dexterous Manipulation