# Domain-Robust Visual Imitation Learning with Mutual Information Constraints

arXiv: https://arxiv.org/abs/2103.05079 · HTML: https://mlsanity.com/paper/2103.05079/

## Key facts

- Cited by 20 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 32 of its 45 references that are in the map.
- First posted on arXiv 8 Mar 2021.

## 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 32)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [MINE: Mutual Information Neural Estimation](https://mlsanity.com/paper/1801.04062/) (2018)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [Towards Principled Methods for Training Generative Adversarial Networks](https://mlsanity.com/paper/1701.04862/) (2017)
- [Time-Contrastive Networks: Self-Supervised Learning from Video](https://mlsanity.com/paper/1704.06888/) (2017)
- [Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization](https://mlsanity.com/paper/1603.00448/) (2016)
- [Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning](https://mlsanity.com/paper/1809.02925/) (2018)
- [Maximum Entropy Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/1507.04888/) (2015)
- [A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models](https://mlsanity.com/paper/1611.03852/) (2016)
- [Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation](https://mlsanity.com/paper/1707.03374/) (2017)
- [Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow](https://mlsanity.com/paper/1810.00821/) (2018)
- [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)
- [Recent Advances in Imitation Learning from Observation](https://mlsanity.com/paper/1905.13566/) (2019)
- [Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning](https://mlsanity.com/paper/1703.02949/) (2017)
- [Third-Person Visual Imitation Learning via Decoupled Hierarchical Controller](https://mlsanity.com/paper/1911.09676/) (2019)
- [Equitability, mutual information, and the maximal information coefficient](https://mlsanity.com/paper/1301.7745/) (2013)
- [Task-Relevant Adversarial Imitation Learning](https://mlsanity.com/paper/1910.01077/) (2019)
- [Learning Dexterous Manipulation for a Soft Robotic Hand from Human Demonstration](https://mlsanity.com/paper/1603.06348/) (2016)
- [Domain-Adversarial and Conditional State Space Model for Imitation Learning](https://mlsanity.com/paper/2001.11628/) (2020)

## Built on by (most cited first, 20 of 20)

- [Teach a Robot to FISH: Versatile Imitation from One Minute of Demonstrations](https://mlsanity.com/paper/2303.01497/) (2023)
- [Watch and Match: Supercharging Imitation with Regularized Optimal Transport](https://mlsanity.com/paper/2206.15469/) (2022)
- [See to Touch: Learning Tactile Dexterity through Visual Incentives](https://mlsanity.com/paper/2309.12300/) (2023)
- [Learn what matters: cross-domain imitation learning with task-relevant embeddings](https://mlsanity.com/paper/2209.12093/) (2022)
- [Adversarial Imitation Learning from Visual Observations using Latent Information](https://mlsanity.com/paper/2309.17371/) (2023)
- [Domain Adaptive Imitation Learning with Visual Observation](https://mlsanity.com/paper/2312.00548/) (2023)
- [SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models](https://mlsanity.com/paper/2306.10695/) (2023)
- [VIEW: Visual Imitation Learning with Waypoints](https://mlsanity.com/paper/2404.17906/) (2024)
- [Visually Robust Adversarial Imitation Learning from Videos with Contrastive Learning](https://mlsanity.com/paper/2407.12792/) (2024)
- [xTED: Cross-Domain Adaptation via Diffusion-Based Trajectory Editing](https://mlsanity.com/paper/2409.08687/) (2024)
- [Cross Domain Robot Imitation with Invariant Representation](https://mlsanity.com/paper/2109.05940/) (2021)
- [Robust Visual Imitation Learning with Inverse Dynamics Representations](https://mlsanity.com/paper/2310.14274/) (2023)
- [Transferable Reward Learning by Dynamics-Agnostic Discriminator Ensemble](https://mlsanity.com/paper/2206.00238/) (2022)
- [Stem-OB: Generalizable Visual Imitation Learning with Stem-Like Convergent Observation through Diffusion Inversion](https://mlsanity.com/paper/2411.04919/) (2024)
- [DIDA: Denoised Imitation Learning based on Domain Adaptation](https://mlsanity.com/paper/2404.03382/) (2024)
- [SENSOR: Imitate Third-Person Expert's Behaviors via Active Sensoring](https://mlsanity.com/paper/2404.03386/) (2024)
- [Beyond Domain Randomization: Event-Inspired Perception for Visually Robust Adversarial Imitation from Videos](https://mlsanity.com/paper/2505.18899/) (2025)
- [Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations](https://mlsanity.com/paper/2502.02867/) (2025)
- [Learning from Observation: A Survey of Recent Advances](https://mlsanity.com/paper/2509.19379/) (2025)
- [Reinforcement Learning from Cross-domain Videos with Video Prediction Model](https://mlsanity.com/paper/2606.03201/) (2026)

## Abstract (arXiv)

Human beings are able to understand objectives and learn by simply observing others perform a task. Imitation learning methods aim to replicate such capabilities, however, they generally depend on access to a full set of optimal states and actions taken with the agent's actuators and from the agent's point of view. In this paper, we introduce a new algorithm - called Disentangling Generative Adversarial Imitation Learning (DisentanGAIL) - with the purpose of bypassing such constraints. Our algorithm enables autonomous agents to learn directly from high dimensional observations of an expert performing a task, by making use of adversarial learning with a latent representation inside the discriminator network. Such latent representation is regularized through mutual information constraints to incentivize learning only features that encode information about the completion levels of the task being demonstrated. This allows to obtain a shared feature space to successfully perform imitation while disregarding the differences between the expert's and the agent's domains. Empirically, our algorithm is able to efficiently imitate in a diverse range of control problems including balancing, manipulation and locomotive tasks, while being robust to various domain differences in terms of both environment appearance and agent embodiment.