# One-Shot Domain-Adaptive Imitation Learning via Progressive Learning

arXiv: https://arxiv.org/abs/2204.11251 · HTML: https://mlsanity.com/paper/2204.11251/

## Key facts

- Cited by 6 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 27 of its 52 references that are in the map.
- First posted on arXiv 24 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, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [An Algorithmic Perspective on Imitation Learning](https://mlsanity.com/paper/1811.06711/) (2018)
- [DualGAN: Unsupervised Dual Learning for Image-to-Image Translation](https://mlsanity.com/paper/1704.02510/) (2017)
- [Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks](https://mlsanity.com/paper/1612.05424/) (2016)
- [Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation](https://mlsanity.com/paper/1710.04615/) (2017)
- [Learning to Discover Cross-Domain Relations with Generative Adversarial Networks](https://mlsanity.com/paper/1703.05192/) (2017)
- [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)
- [Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1607.03516/) (2016)
- [The Surprising Effectiveness of Representation Learning for Visual Imitation](https://mlsanity.com/paper/2112.01511/) (2021)
- [Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping](https://mlsanity.com/paper/1709.07857/) (2017)
- [AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos](https://mlsanity.com/paper/1912.04443/) (2019)
- [Lifelong GAN: Continual Learning for Conditional Image Generation](https://mlsanity.com/paper/1907.10107/) (2019)
- [Multiple Interactions Made Easy (MIME): Large Scale Demonstrations Data for Imitation](https://mlsanity.com/paper/1810.07121/) (2018)
- [One-Shot Generalization in Deep Generative Models](https://mlsanity.com/paper/1603.05106/) (2016)
- [Visual Closed-Loop Control for Pouring Liquids](https://mlsanity.com/paper/1610.02610/) (2016)
- [Tactile Sim-to-Real Policy Transfer via Real-to-Sim Image Translation](https://mlsanity.com/paper/2106.08796/) (2021)
- [Manipulator-Independent Representations for Visual Imitation](https://mlsanity.com/paper/2103.09016/) (2021)
- [Explainable Hierarchical Imitation Learning for Robotic Drink Pouring](https://mlsanity.com/paper/2105.07348/) (2021)
- [Human-Robot Shared Control for Surgical Robot Based on Context-Aware Sim-to-Real Adaptation](https://mlsanity.com/paper/2204.11116/) (2022)
- [Feedback Motion Planning for Liquid Transfer using Supervised Learning](https://mlsanity.com/paper/1609.03433/) (2016)

## Built on by (most cited first, 6 of 6)

- [Simulations of Common Unsupervised Domain Adaptation Algorithms for Image Classification](https://mlsanity.com/paper/2502.10694/) (2025)
- [Cloud-Based Hierarchical Imitation Learning for Scalable Transfer of Construction Skills from Human Workers to Assisting Robots](https://mlsanity.com/paper/2309.11619/) (2023)
- [Imitation learning for sim-to-real transfer of robotic cutting policies based on residual Gaussian process disturbance force model](https://mlsanity.com/paper/2311.04096/) (2023)
- [End-to-end example-based sim-to-real RL policy transfer based on neural stylisation with application to robotic cutting](https://mlsanity.com/paper/2601.20846/) (2026)
- [Hierarchical Audio-Visual-Proprioceptive Fusion for Precise Robotic Manipulation](https://mlsanity.com/paper/2602.13640/) (2026)
- [Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing](https://mlsanity.com/paper/2606.11577/) (2026)

## Abstract (arXiv)

Traditional deep learning-based visual imitation learning techniques require a large amount of demonstration data for model training, and the pre-trained models are difficult to adapt to new scenarios. To address these limitations, we propose a unified framework using a novel progressive learning approach comprised of three phases: i) a coarse learning phase for concept representation, ii) a fine learning phase for action generation, and iii) an imaginary learning phase for domain adaptation. Overall, this approach leads to a one-shot domain-adaptive imitation learning framework. We use robotic pouring task as an example to evaluate its effectiveness. Our results show that the method has several advantages over contemporary end-to-end imitation learning approaches, including an improved success rate for task execution and more efficient training for deep imitation learning. In addition, the generalizability to new domains is improved, as demonstrated here with novel background, target container and granule combinations. We believe that the proposed method can be broadly applicable to different industrial or domestic applications that involve deep imitation learning for robotic manipulation, where the target scenarios have high diversity while the human demonstration data is limited.