# Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning

arXiv: https://arxiv.org/abs/2204.02863 · HTML: https://mlsanity.com/paper/2204.02863/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 20 of its 32 references that are in the map.
- First posted on arXiv 6 Apr 2022.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Builds on (most cited first, 20 of 20)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning](https://mlsanity.com/paper/2202.02005/) (2022)
- [An Algorithmic Perspective on Imitation Learning](https://mlsanity.com/paper/1811.06711/) (2018)
- [Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards](https://mlsanity.com/paper/1707.08817/) (2017)
- [Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation](https://mlsanity.com/paper/1710.04615/) (2017)
- [Residual Reinforcement Learning for Robot Control](https://mlsanity.com/paper/1812.03201/) (2018)
- [One-Shot Imitation Learning](https://mlsanity.com/paper/1703.07326/) (2017)
- [One-Shot Visual Imitation Learning via Meta-Learning](https://mlsanity.com/paper/1709.04905/) (2017)
- [Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration](https://mlsanity.com/paper/2105.06411/) (2021)
- [You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration](https://mlsanity.com/paper/2201.12716/) (2022)
- [BlenderProc](https://mlsanity.com/paper/1911.01911/) (2019)
- [Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning](https://mlsanity.com/paper/2005.10872/) (2020)
- [Learning Multi-Stage Tasks with One Demonstration via Self-Replay](https://mlsanity.com/paper/2111.07447/) (2021)
- [FlowControl: Optical Flow Based Visual Servoing](https://mlsanity.com/paper/2007.00291/) (2020)
- [Siamese Convolutional Neural Network for Sub-millimeter-accurate Camera Pose Estimation and Visual Servoing](https://mlsanity.com/paper/1903.04713/) (2019)
- [Coarse-to-Fine for Sim-to-Real: Sub-Millimetre Precision Across Wide Task Spaces](https://mlsanity.com/paper/2105.11283/) (2021)

## Built on by (most cited first, 30 of 38)

- [Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation](https://mlsanity.com/paper/2401.02117/) (2024)
- [MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations](https://mlsanity.com/paper/2310.17596/) (2023)
- [In-Context Imitation Learning via Next-Token Prediction](https://mlsanity.com/paper/2408.15980/) (2024)
- [OKAMI: Teaching Humanoid Robots Manipulation Skills through Single Video Imitation](https://mlsanity.com/paper/2410.11792/) (2024)
- [Robot See Robot Do: Imitating Articulated Object Manipulation with Monocular 4D Reconstruction](https://mlsanity.com/paper/2409.18121/) (2024)
- [SkillMimicGen: Automated Demonstration Generation for Efficient Skill Learning and Deployment](https://mlsanity.com/paper/2410.18907/) (2024)
- [Vision-based Manipulation from Single Human Video with Open-World Object Graphs](https://mlsanity.com/paper/2405.20321/) (2024)
- [DINOBot: Robot Manipulation via Retrieval and Alignment with Vision Foundation Models](https://mlsanity.com/paper/2402.13181/) (2024)
- [Robotic Manipulation by Imitating Generated Videos Without Physical Demonstrations](https://mlsanity.com/paper/2507.00990/) (2025)
- [Crossing the Human-Robot Embodiment Gap with Sim-to-Real RL using One Human Demonstration](https://mlsanity.com/paper/2504.12609/) (2025)
- [DITTO: Demonstration Imitation by Trajectory Transformation](https://mlsanity.com/paper/2403.15203/) (2024)
- [One-Shot Imitation Learning: A Pose Estimation Perspective](https://mlsanity.com/paper/2310.12077/) (2023)
- [DemoDiffusion: One-Shot Human Imitation using pre-trained Diffusion Policy](https://mlsanity.com/paper/2506.20668/) (2025)
- [Composable Part-Based Manipulation](https://mlsanity.com/paper/2405.05876/) (2024)
- [On the Effectiveness of Retrieval, Alignment, and Replay in Manipulation](https://mlsanity.com/paper/2312.12345/) (2023)
- [Few-Shot In-Context Imitation Learning via Implicit Graph Alignment](https://mlsanity.com/paper/2310.12238/) (2023)
- [ILBiT: Imitation Learning for Robot Using Position and Torque Information based on Bilateral Control with Transformer](https://mlsanity.com/paper/2401.16653/) (2024)
- [MILES: Making Imitation Learning Easy with Self-Supervision](https://mlsanity.com/paper/2410.19693/) (2024)
- [ForceSight: Text-Guided Mobile Manipulation with Visual-Force Goals](https://mlsanity.com/paper/2309.12312/) (2023)
- [NOD-TAMP: Generalizable Long-Horizon Planning with Neural Object Descriptors](https://mlsanity.com/paper/2311.01530/) (2023)
- [One-Shot Dual-Arm Imitation Learning](https://mlsanity.com/paper/2503.06831/) (2025)
- [RoboSSM: Scalable In-context Imitation Learning via State-Space Models](https://mlsanity.com/paper/2509.19658/) (2025)
- [Conditional Visual Servoing for Multi-Step Tasks](https://mlsanity.com/paper/2205.08441/) (2022)
- [Imitation Learning-based Visual Servoing for Tracking Moving Objects](https://mlsanity.com/paper/2309.07729/) (2023)
- [Expert Proximity as Surrogate Rewards for Single Demonstration Imitation Learning](https://mlsanity.com/paper/2402.01057/) (2024)
- [Learning in ImaginationLand: Omnidirectional Policies through 3D Generative Models (OP-Gen)](https://mlsanity.com/paper/2509.06191/) (2025)
- [ReMoBot: Retrieval-Based Few-Shot Imitation Learning for Mobile Manipulation with Vision Foundation Models](https://mlsanity.com/paper/2408.15919/) (2024)
- [Annotation-Free One-Shot Imitation Learning for Multi-Step Manipulation Tasks](https://mlsanity.com/paper/2509.24972/) (2025)
- [One Demonstration Is Enough for Real-World Robotic Reinforcement Learning](https://mlsanity.com/paper/2607.01651/) (2026)
- [POIL: Point-based One-Shot Imitation Learning with Stable Dynamical Systems](https://mlsanity.com/paper/2609.30404/) (2026)

## Abstract (arXiv)

We present DOME, a novel method for one-shot imitation learning, where a task can be learned from just a single demonstration and then be deployed immediately, without any further data collection or training. DOME does not require prior task or object knowledge, and can perform the task in novel object configurations and with distractors. At its core, DOME uses an image-conditioned object segmentation network followed by a learned visual servoing network, to move the robot's end-effector to the same relative pose to the object as during the demonstration, after which the task can be completed by replaying the demonstration's end-effector velocities. We show that DOME achieves near 100% success rate on 7 real-world everyday tasks, and we perform several studies to thoroughly understand each individual component of DOME. Videos and supplementary material are available at: https://www.robot-learning.uk/dome .