# CRIL: Continual Robot Imitation Learning via Generative and Prediction Model

arXiv: https://arxiv.org/abs/2106.09422 · HTML: https://mlsanity.com/paper/2106.09422/

## Key facts

- Cited by 24 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 25 of its 35 references that are in the map.
- First posted on arXiv 17 Jun 2021.

## 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, 25 of 25)

- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning](https://mlsanity.com/paper/1910.10897/) (2019)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [MoCoGAN: Decomposing Motion and Content for Video Generation](https://mlsanity.com/paper/1707.04993/) (2017)
- [An Algorithmic Perspective on Imitation Learning](https://mlsanity.com/paper/1811.06711/) (2018)
- [Policy Distillation](https://mlsanity.com/paper/1511.06295/) (2015)
- [Generating Videos with Scene Dynamics](https://mlsanity.com/paper/1609.02612/) (2016)
- [Measuring Catastrophic Forgetting in Neural Networks](https://mlsanity.com/paper/1708.02072/) (2017)
- [Deep multi-scale video prediction beyond mean square error](https://mlsanity.com/paper/1511.05440/) (2015)
- [Deep Visual Foresight for Planning Robot Motion](https://mlsanity.com/paper/1610.00696/) (2016)
- [Stochastic Video Generation with a Learned Prior](https://mlsanity.com/paper/1802.07687/) (2018)
- [Memory Efficient Experience Replay for Streaming Learning](https://mlsanity.com/paper/1809.05922/) (2018)
- [Reinforcement and Imitation Learning for Diverse Visuomotor Skills](https://mlsanity.com/paper/1802.09564/) (2018)
- [Continual Learning for Robotics: Definition, Framework, Learning Strategies, Opportunities and Challenges](https://mlsanity.com/paper/1907.00182/) (2019)
- [To Create What You Tell: Generating Videos from Captions](https://mlsanity.com/paper/1804.08264/) (2018)
- [Generative Models from the perspective of Continual Learning](https://mlsanity.com/paper/1812.09111/) (2018)
- [Deep Generative Dual Memory Network for Continual Learning](https://mlsanity.com/paper/1710.10368/) (2017)
- [One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks](https://mlsanity.com/paper/1810.11043/) (2018)
- [Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer](https://mlsanity.com/paper/1906.04452/) (2019)
- [Marginal Replay vs Conditional Replay for Continual Learning](https://mlsanity.com/paper/1810.12069/) (2018)
- [Unicorn: Continual Learning with a Universal, Off-policy Agent](https://mlsanity.com/paper/1802.08294/) (2018)
- [Continual State Representation Learning for Reinforcement Learning using Generative Replay](https://mlsanity.com/paper/1810.03880/) (2018)
- [Rethinking Continual Learning for Autonomous Agents and Robots](https://mlsanity.com/paper/1907.01929/) (2019)

## Built on by (most cited first, 24 of 24)

- [LOTUS: Continual Imitation Learning for Robot Manipulation Through Unsupervised Skill Discovery](https://mlsanity.com/paper/2311.02058/) (2023)
- [VLA-OS: Structuring and Dissecting Planning Representations and Paradigms in Vision-Language-Action Models](https://mlsanity.com/paper/2506.17561/) (2025)
- [Online Continual Learning For Interactive Instruction Following Agents](https://mlsanity.com/paper/2403.07548/) (2024)
- [Dexterous Manipulation through Imitation Learning: A Survey](https://mlsanity.com/paper/2504.03515/) (2025)
- [Incremental Learning of Retrievable Skills For Efficient Continual Task Adaptation](https://mlsanity.com/paper/2410.22658/) (2024)
- [Continual Learning from Demonstration of Robotics Skills](https://mlsanity.com/paper/2202.06843/) (2022)
- [t-DGR: A Trajectory-Based Deep Generative Replay Method for Continual Learning in Decision Making](https://mlsanity.com/paper/2401.02576/) (2024)
- [M2Distill: Multi-Modal Distillation for Lifelong Imitation Learning](https://mlsanity.com/paper/2410.00064/) (2024)
- [Never-Ending Behavior-Cloning Agent for Robotic Manipulation](https://mlsanity.com/paper/2403.00336/) (2024)
- [SPECI: Skill Prompts based Hierarchical Continual Imitation Learning for Robot Manipulation](https://mlsanity.com/paper/2504.15561/) (2025)
- [Hierarchical-Task-Aware Multi-modal Mixture of Incremental LoRA Experts for Embodied Continual Learning](https://mlsanity.com/paper/2506.04595/) (2025)
- [Scalable and Efficient Continual Learning from Demonstration via a Hypernetwork-generated Stable Dynamics Model](https://mlsanity.com/paper/2311.03600/) (2023)
- [Stable Continual Reinforcement Learning via Diffusion-based Trajectory Replay](https://mlsanity.com/paper/2411.10809/) (2024)
- [Solving Continual Offline RL through Selective Weights Activation on Aligned Spaces](https://mlsanity.com/paper/2410.15698/) (2024)
- [Evaluating Continual Learning on a Home Robot](https://mlsanity.com/paper/2306.02413/) (2023)
- [Fusion Dynamical Systems with Machine Learning in Imitation Learning: A Comprehensive Overview](https://mlsanity.com/paper/2403.19916/) (2024)
- [Continual Diffuser (CoD): Mastering Continual Offline Reinforcement Learning with Experience Rehearsal](https://mlsanity.com/paper/2409.02512/) (2024)
- [SPREAD: Subspace Representation Distillation for Lifelong Imitation Learning](https://mlsanity.com/paper/2603.08763/) (2026)
- [Lifelong Imitation Learning with Multimodal Latent Replay and Incremental Adjustment](https://mlsanity.com/paper/2603.10929/) (2026)
- [Learning New Tasks via Reusable Skills: Skill-Compositional Experts for Embodied Continual Learning](https://mlsanity.com/paper/2606.15685/) (2026)
- [LiMoDE: Rethinking Lifelong Robot Manipulation from a Mixture-of-Dynamic-Experts Perspective](https://mlsanity.com/paper/2606.26183/) (2026)
- [Evaluating Pretrained models for Deployable Lifelong Learning](https://mlsanity.com/paper/2311.13648/) (2023)
- [CMIP-CIL: A Cross-Modal Benchmark for Image-Point Class Incremental Learning](https://mlsanity.com/paper/2504.08422/) (2025)
- [Data-Free Continual Learning for World Action Model with Rectified Generative Replay](https://mlsanity.com/paper/2610.18038/) (2026)

## Abstract (arXiv)

Imitation learning (IL) algorithms have shown promising results for robots to learn skills from expert demonstrations. However, they need multi-task demonstrations to be provided at once for acquiring diverse skills, which is difficult in real world. In this work we study how to realize continual imitation learning ability that empowers robots to continually learn new tasks one by one, thus reducing the burden of multi-task IL and accelerating the process of new task learning at the same time. We propose a novel trajectory generation model that employs both a generative adversarial network and a dynamics-aware prediction model to generate pseudo trajectories from all learned tasks in the new task learning process. Our experiments on both simulation and real-world manipulation tasks demonstrate the effectiveness of our method.