# Visually Robust Adversarial Imitation Learning from Videos with Contrastive Learning

arXiv: https://arxiv.org/abs/2407.12792 · HTML: https://mlsanity.com/paper/2407.12792/

## Key facts

- Cited by 9 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 40 of its 51 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## 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 40)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Learning Latent Dynamics for Planning from Pixels](https://mlsanity.com/paper/1811.04551/) (2018)
- [DeepMind Control Suite](https://mlsanity.com/paper/1801.00690/) (2018)
- [Learning Robust Rewards with Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1710.11248/) (2017)
- [Time-Contrastive Networks: Self-Supervised Learning from Video](https://mlsanity.com/paper/1704.06888/) (2017)
- [Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning](https://mlsanity.com/paper/2107.09645/) (2021)
- [Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning](https://mlsanity.com/paper/1809.02925/) (2018)
- [A Divergence Minimization Perspective on Imitation Learning Methods](https://mlsanity.com/paper/1911.02256/) (2019)
- [Generative Adversarial Imitation from Observation](https://mlsanity.com/paper/1807.06158/) (2018)
- [Imitation Learning via Off-Policy Distribution Matching](https://mlsanity.com/paper/1912.05032/) (2019)
- [Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation](https://mlsanity.com/paper/1707.03374/) (2017)
- [AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos](https://mlsanity.com/paper/1912.04443/) (2019)
- [Third-Person Imitation Learning](https://mlsanity.com/paper/1703.01703/) (2017)
- [Off-Policy Imitation Learning from Observations](https://mlsanity.com/paper/2102.13185/) (2021)
- [VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learning](https://mlsanity.com/paper/2202.10324/) (2022)
- [Cross-Domain Imitation Learning via Optimal Transport](https://mlsanity.com/paper/2110.03684/) (2021)
- [Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement](https://mlsanity.com/paper/1910.04417/) (2019)
- [MobILE: Model-Based Imitation Learning From Observation Alone](https://mlsanity.com/paper/2102.10769/) (2021)
- [Cross-domain Imitation from Observations](https://mlsanity.com/paper/2105.10037/) (2021)
- [Visual Adversarial Imitation Learning using Variational Models](https://mlsanity.com/paper/2107.08829/) (2021)
- [Sample-Efficient Imitation Learning via Generative Adversarial Nets](https://mlsanity.com/paper/1809.02064/) (2018)
- [Learning Belief Representations for Imitation Learning in POMDPs](https://mlsanity.com/paper/1906.09510/) (2019)

## Built on by (most cited first, 9 of 9)

- [Posterior Behavioral Cloning: Pretraining BC Policies for Efficient RL Finetuning](https://mlsanity.com/paper/2512.16911/) (2025)
- [Videos are Sample-Efficient Supervisions: Behavior Cloning from Videos via Latent Representations](https://mlsanity.com/paper/2512.21586/) (2025)
- [Spatially Visual Perception for End-to-End Robotic Learning](https://mlsanity.com/paper/2411.17458/) (2024)
- [Dual-Granularity Contrastive Reward via Generated Episodic Guidance for Efficient Embodied RL](https://mlsanity.com/paper/2602.12636/) (2026)
- [Beyond Domain Randomization: Event-Inspired Perception for Visually Robust Adversarial Imitation from Videos](https://mlsanity.com/paper/2505.18899/) (2025)
- [Robust Point Cloud Reinforcement Learning via PCA-Based Canonicalization](https://mlsanity.com/paper/2510.20974/) (2025)
- [Semi-Supervised Cross-Domain Imitation Learning](https://mlsanity.com/paper/2602.10793/) (2026)
- [Teacher-Student Representational Alignment for Reinforcement Learning-Driven Imitation Learning](https://mlsanity.com/paper/2605.28372/) (2026)
- [Reinforcement Learning from Cross-domain Videos with Video Prediction Model](https://mlsanity.com/paper/2606.03201/) (2026)

## Abstract (arXiv)

We propose C-LAIfO, a computationally efficient algorithm designed for imitation learning from videos in the presence of visual mismatch between agent and expert domains. We analyze the problem of imitation from expert videos with visual discrepancies, and introduce a solution for robust latent space estimation using contrastive learning and data augmentation. Provided a visually robust latent space, our algorithm performs imitation entirely within this space using off-policy adversarial imitation learning. We conduct a thorough ablation study to justify our design and test C-LAIfO on high-dimensional continuous robotic tasks. Additionally, we demonstrate how C-LAIfO can be combined with other reward signals to facilitate learning on a set of challenging hand manipulation tasks with sparse rewards. Our experiments show improved performance compared to baseline methods, highlighting the effectiveness of C-LAIfO. To ensure reproducibility, we open source our code.