mlsanityData to 7 Oct 2026

Learning One-Shot Imitation from Humans without Humans

arXiv 1911.01103 ↗First posted 4 Nov 2019cs.RO cs.AI cs.CVcited by 32

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

Abstract (arXiv)

Humans can naturally learn to execute a new task by seeing it performed by other individuals once, and then reproduce it in a variety of configurations. Endowing robots with this ability of imitating humans from third person is a very immediate and natural way of teaching new tasks. Only recently, through meta-learning, there have been successful attempts to one-shot imitation learning from humans; however, these approaches require a lot of human resources to collect the data in the real world to train the robot. But is there a way to remove the need for real world human demonstrations during training? We show that with Task-Embedded Control Networks, we can infer control polices by embedding human demonstrations that can condition a control policy and achieve one-shot imitation learning. Importantly, we do not use a real human arm to supply demonstrations during training, but instead leverage domain randomisation in an application that has not been seen before: sim-to-real transfer on humans. Upon evaluating our approach on pushing and placing tasks in both simulation and in the real world, we show that in comparison to a system that was trained on real-world data we are able to achieve similar results by utilising only simulation data.

Key facts. Cited by 32 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 4 Nov 2019.

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Citing papers per year: 2021: 7; 2022: 6; 2023: 8; 2024: 6; 2025: 5.

32 papers.

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  1. 2022BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learningcited by 517
  2. 2021MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scalecited by 226
  3. 2023RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shotcited by 173
  4. 2021Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisationcited by 120
  5. 2021Learning Generalizable Robotic Reward Functions from "In-The-Wild" Human Videoscited by 116
  6. 2023Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysiscited by 99
  7. 2024Robot See Robot Do: Imitating Articulated Object Manipulation with Monocular 4D Reconstructioncited by 51
  8. 2024VLM See, Robot Do: Human Demo Video to Robot Action Plan via Vision Language Modelcited by 36
  9. 2021Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platformscited by 22
  10. 2023One-Shot Imitation Learning: A Pose Estimation Perspectivecited by 21
  11. 2022On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learningcited by 18
  12. 2023Learning Video-Conditioned Policies for Unseen Manipulation Taskscited by 15
20 more
  1. 2022Learning Reward Functions for Robotic Manipulation by Observing Humanscited by 14
  2. 2025A Survey on Imitation Learning for Contact-Rich Tasks in Roboticscited by 12
  3. 2023Aligning Step-by-Step Instructional Diagrams to Video Demonstrationscited by 10
  4. 2023EC^2: Emergent Communication for Embodied Controlcited by 10
  5. 2023Guide Your Agent with Adaptive Multimodal Rewardscited by 10
  6. 2024ILBiT: Imitation Learning for Robot Using Position and Torque Information based on Bilateral Control with Transformercited by 10
  7. 2024Learning by Watching: A Review of Video-based Learning Approaches for Robot Manipulationcited by 10
  8. 2024Effective Tuning Strategies for Generalist Robot Manipulation Policiescited by 10
  9. 2024Robo-MUTUAL: Robotic Multimodal Task Specification via Unimodal Learningcited by 6
  10. 2023One-shot Visual Imitation via Attributed Waypoints and Demonstration Augmentationcited by 4
  11. 2025See Once, Then Act: Vision-Language-Action Model with Task Learning from One-Shot Video Demonstrationscited by 4
  12. 2022Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learningcited by 3
  13. 2021A Simple Approach to Continual Learning by Transferring Skill Parameterscited by 2
  14. 2021Stage Conscious Attention Network (SCAN) : A Demonstration-Conditioned Policy for Few-Shot Imitationcited by 2
  15. 2022Randomized-to-Canonical Model Predictive Control for Real-world Visual Robotic Manipulationcited by 2
  16. 2021Infinite-dimensional Folded-in-time Deep Neural Networkscited by 0
  17. 2022GloCAL: Glocalized Curriculum-Aided Learning of Multiple Tasks with Application to Robotic Graspingcited by 0
  18. 2025GIFT: Geometry-Induced Functional Transfer for Category-level Object Manipulationcited by 0
  19. 2025Generative Grasp Detection and Estimation with Concept Learning-based Safety Criteriacited by 0
  20. 2025ManiLong-Shot: Interaction-Aware One-Shot Imitation Learning for Long-Horizon Manipulationcited by 0

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  1. 2024VITAL: Interactive Few-Shot Imitation Learning via Visual Human-in-the-Loop Corrections
  2. 2018Task-Embedded Control Networks for Few-Shot Imitation Learning
  3. 2026Scaling Single Human Demonstrations for Imitation Learning using Generative Foundational Models
  4. 2021Towards More Generalizable One-shot Visual Imitation Learning
  5. 2023MimicPlay: Long-Horizon Imitation Learning by Watching Human Play
  6. 2024Meta-Controller: Few-Shot Imitation of Unseen Embodiments and Tasks in Continuous Control
  7. 2026Human-to-Robot Interaction: Learning from Video Demonstration for Robot Imitation
  8. 2021Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration