# CCIL: Continuity-based Data Augmentation for Corrective Imitation Learning

arXiv: https://arxiv.org/abs/2310.12972 · HTML: https://mlsanity.com/paper/2310.12972/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 37 of its 53 references that are in the map.
- First posted on arXiv 19 Oct 2023.

## Placed in

- Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/)

## Builds on (most cited first, 30 of 37)

- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [D4RL: Datasets for Deep Data-Driven Reinforcement Learning](https://mlsanity.com/paper/2004.07219/) (2020)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [Spectrally-normalized margin bounds for neural networks](https://mlsanity.com/paper/1706.08498/) (2017)
- [MOReL : Model-Based Offline Reinforcement Learning](https://mlsanity.com/paper/2005.05951/) (2020)
- [Learning Robust Rewards with Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1710.11248/) (2017)
- [Implicit Behavioral Cloning](https://mlsanity.com/paper/2109.00137/) (2021)
- [Learning Invariant Representations for Reinforcement Learning without Reconstruction](https://mlsanity.com/paper/2006.10742/) (2020)
- [Causal Confusion in Imitation Learning](https://mlsanity.com/paper/1905.11979/) (2019)
- [DART: Noise Injection for Robust Imitation Learning](https://mlsanity.com/paper/1703.09327/) (2017)
- [Benchmarking Model-Based Reinforcement Learning](https://mlsanity.com/paper/1907.02057/) (2019)
- [Imitation Learning as f-Divergence Minimization](https://mlsanity.com/paper/1905.12888/) (2019)
- [Self-Supervised Correspondence in Visuomotor Policy Learning](https://mlsanity.com/paper/1909.06933/) (2019)
- [Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration](https://mlsanity.com/paper/1707.02920/) (2017)
- [Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction](https://mlsanity.com/paper/1703.01030/) (2017)
- [Lipschitz Continuity in Model-based Reinforcement Learning](https://mlsanity.com/paper/1804.07193/) (2018)
- [Neural Lander: Stable Drone Landing Control using Learned Dynamics](https://mlsanity.com/paper/1811.08027/) (2018)
- [Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap](https://mlsanity.com/paper/2103.03236/) (2021)
- [Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Control](https://mlsanity.com/paper/2103.02142/) (2021)
- [Feedback in Imitation Learning: The Three Regimes of Covariate Shift](https://mlsanity.com/paper/2102.02872/) (2021)
- [Denoised MDPs: Learning World Models Better Than the World Itself](https://mlsanity.com/paper/2206.15477/) (2022)
- [NeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesis](https://mlsanity.com/paper/2301.08556/) (2023)
- [Learning to Search Better Than Your Teacher](https://mlsanity.com/paper/1502.02206/) (2015)
- [ContactNets: Learning Discontinuous Contact Dynamics with Smooth, Implicit Representations](https://mlsanity.com/paper/2009.11193/) (2020)
- [Grasping with Chopsticks: Combating Covariate Shift in Model-free Imitation Learning for Fine Manipulation](https://mlsanity.com/paper/2011.06719/) (2020)
- [What can I do here? A Theory of Affordances in Reinforcement Learning](https://mlsanity.com/paper/2006.15085/) (2020)
- [Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior](https://mlsanity.com/paper/2307.14619/) (2023)

## Built on by (most cited first, 29 of 29)

- [Vision-Language-Action Models for Robotics: A Review Towards Real-World Applications](https://mlsanity.com/paper/2510.07077/) (2025)
- [RACER: Rich Language-Guided Failure Recovery Policies for Imitation Learning](https://mlsanity.com/paper/2409.14674/) (2024)
- [RaC: Robot Learning for Long-Horizon Tasks by Scaling Recovery and Correction](https://mlsanity.com/paper/2509.07953/) (2025)
- [JUICER: Data-Efficient Imitation Learning for Robotic Assembly](https://mlsanity.com/paper/2404.03729/) (2024)
- [Diffusion Meets DAgger: Supercharging Eye-in-hand Imitation Learning](https://mlsanity.com/paper/2402.17768/) (2024)
- [SAFE-GIL: SAFEty Guided Imitation Learning for Robotic Systems](https://mlsanity.com/paper/2404.05249/) (2024)
- [Structured Agent Distillation for Large Language Model](https://mlsanity.com/paper/2505.13820/) (2025)
- [MILES: Making Imitation Learning Easy with Self-Supervision](https://mlsanity.com/paper/2410.19693/) (2024)
- [From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning](https://mlsanity.com/paper/2603.10263/) (2026)
- [Emergent Dexterity via Diverse Resets and Large-Scale Reinforcement Learning](https://mlsanity.com/paper/2603.15789/) (2026)
- [Elastic Motion Policy: An Adaptive Dynamical System for Robust and Efficient One-Shot Imitation Learning](https://mlsanity.com/paper/2503.08029/) (2025)
- [D-CODA: Diffusion for Coordinated Dual-Arm Data Augmentation](https://mlsanity.com/paper/2505.04860/) (2025)
- [Using Non-Expert Data to Robustify Imitation Learning via Offline Reinforcement Learning](https://mlsanity.com/paper/2510.19495/) (2025)
- [FAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvement](https://mlsanity.com/paper/2607.01111/) (2026)
- [CRAFT: Video Diffusion for Bimanual Robot Data Generation](https://mlsanity.com/paper/2604.03552/) (2026)
- [ROPA: Synthetic Robot Pose Generation for RGB-D Bimanual Data Augmentation](https://mlsanity.com/paper/2509.19454/) (2025)
- [Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation](https://mlsanity.com/paper/2602.08557/) (2026)
- [Dense Dynamics-Aware Reward Synthesis: Integrating Prior Experience with Demonstrations](https://mlsanity.com/paper/2412.01114/) (2024)
- [Robot Self-Improvement via Human-Video Dynamics Models](https://mlsanity.com/paper/2606.21406/) (2026)
- [Unforgettable Lessons from Forgettable Images: Intra-Class Memorability Matters in Computer Vision](https://mlsanity.com/paper/2412.20761/) (2024)
- [Safety-Aware Imitation Learning via MPC-Guided Disturbance Injection](https://mlsanity.com/paper/2508.03129/) (2025)
- [Exploiting Policy Idling for Dexterous Manipulation](https://mlsanity.com/paper/2508.15669/) (2025)
- [GrOMP: Grasped Object Manifold Projection for Multimodal Imitation Learning of Manipulation](https://mlsanity.com/paper/2512.03347/) (2025)
- [Difference-Aware Retrieval Policies for Imitation Learning](https://mlsanity.com/paper/2606.09758/) (2026)
- [Denoising-based Contractive Imitation Learning](https://mlsanity.com/paper/2503.15918/) (2025)
- [How to Mitigate the Distribution Shift Problem in Robotics Control: A Robust and Adaptive Approach Based on Offline to Online Imitation Learning](https://mlsanity.com/paper/2605.25414/) (2026)
- [ReGuide: From Test-Time Guidance to Self-Improving Diffusion Policies](https://mlsanity.com/paper/2606.28939/) (2026)
- [Augmentations for Robust and Efficient Imitation Learning in Streamed Video Games](https://mlsanity.com/paper/2607.14200/) (2026)
- [NEEDLEWORK: Offline Rewriting of Robot Data with Verified Local Stitches](https://mlsanity.com/paper/2610.02339/) (2026)

## Abstract (arXiv)

We present a new technique to enhance the robustness of imitation learning methods by generating corrective data to account for compounding errors and disturbances. While existing methods rely on interactive expert labeling, additional offline datasets, or domain-specific invariances, our approach requires minimal additional assumptions beyond access to expert data. The key insight is to leverage local continuity in the environment dynamics to generate corrective labels. Our method first constructs a dynamics model from the expert demonstration, encouraging local Lipschitz continuity in the learned model. In locally continuous regions, this model allows us to generate corrective labels within the neighborhood of the demonstrations but beyond the actual set of states and actions in the dataset. Training on this augmented data enhances the agent's ability to recover from perturbations and deal with compounding errors. We demonstrate the effectiveness of our generated labels through experiments in a variety of robotics domains in simulation that have distinct forms of continuity and discontinuity, including classic control problems, drone flying, navigation with high-dimensional sensor observations, legged locomotion, and tabletop manipulation.