# Adversarial Imitation Learning from Video using a State Observer

arXiv: https://arxiv.org/abs/2202.00243 · HTML: https://mlsanity.com/paper/2202.00243/

## Key facts

- Cited by 10 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 22 of its 35 references that are in the map.
- First posted on arXiv 1 Feb 2022.

## Placed in

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

## Builds on (most cited first, 22 of 22)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels](https://mlsanity.com/paper/2004.13649/) (2020)
- [Behavioral Cloning from Observation](https://mlsanity.com/paper/1805.01954/) (2018)
- [Reinforcement Learning with Augmented Data](https://mlsanity.com/paper/2004.14990/) (2020)
- [Time-Contrastive Networks: Self-Supervised Learning from Video](https://mlsanity.com/paper/1704.06888/) (2017)
- [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)
- [Primal Wasserstein Imitation Learning](https://mlsanity.com/paper/2006.04678/) (2020)
- [Third-Person Imitation Learning](https://mlsanity.com/paper/1703.01703/) (2017)
- [Imitating Latent Policies from Observation](https://mlsanity.com/paper/1805.07914/) (2018)
- [Recent Advances in Imitation Learning from Observation](https://mlsanity.com/paper/1905.13566/) (2019)
- [Reinforcement Learning with Videos: Combining Offline Observations with Interaction](https://mlsanity.com/paper/2011.06507/) (2020)
- [Off-Policy Imitation Learning from Observations](https://mlsanity.com/paper/2102.13185/) (2021)
- [VOILA: Visual-Observation-Only Imitation Learning for Autonomous Navigation](https://mlsanity.com/paper/2105.09371/) (2021)
- [Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning](https://mlsanity.com/paper/1703.03078/) (2017)
- [Imitation Learning from Video by Leveraging Proprioception](https://mlsanity.com/paper/1905.09335/) (2019)
- [Learning Transferable Policies for Monocular Reactive MAV Control](https://mlsanity.com/paper/1608.00627/) (2016)
- [DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation](https://mlsanity.com/paper/2104.00163/) (2021)
- [RAIL: A modular framework for Reinforcement-learning-based Adversarial Imitation Learning](https://mlsanity.com/paper/2105.03756/) (2021)

## Built on by (most cited first, 10 of 10)

- [Learning to Act from Actionless Videos through Dense Correspondences](https://mlsanity.com/paper/2310.08576/) (2023)
- [Vision-based Manipulation from Single Human Video with Open-World Object Graphs](https://mlsanity.com/paper/2405.20321/) (2024)
- [K-VIL: Keypoints-based Visual Imitation Learning](https://mlsanity.com/paper/2209.03277/) (2022)
- [VideoAgent: Self-Improving Video Generation](https://mlsanity.com/paper/2410.10076/) (2024)
- [STERLING: Self-Supervised Terrain Representation Learning from Unconstrained Robot Experience](https://mlsanity.com/paper/2309.15302/) (2023)
- [Adversarial Imitation Learning from Visual Observations using Latent Information](https://mlsanity.com/paper/2309.17371/) (2023)
- [Videos are Sample-Efficient Supervisions: Behavior Cloning from Videos via Latent Representations](https://mlsanity.com/paper/2512.21586/) (2025)
- [NOLO: Navigate Only Look Once](https://mlsanity.com/paper/2408.01384/) (2024)
- [Dual-Granularity Contrastive Reward via Generated Episodic Guidance for Efficient Embodied RL](https://mlsanity.com/paper/2602.12636/) (2026)
- [On Generalization and Distributional Update for Mimicking Observations with Adequate Exploration](https://mlsanity.com/paper/2501.12785/) (2025)

## Abstract (arXiv)

The imitation learning research community has recently made significant progress towards the goal of enabling artificial agents to imitate behaviors from video demonstrations alone. However, current state-of-the-art approaches developed for this problem exhibit high sample complexity due, in part, to the high-dimensional nature of video observations. Towards addressing this issue, we introduce here a new algorithm called Visual Generative Adversarial Imitation from Observation using a State Observer VGAIfO-SO. At its core, VGAIfO-SO seeks to address sample inefficiency using a novel, self-supervised state observer, which provides estimates of lower-dimensional proprioceptive state representations from high-dimensional images. We show experimentally in several continuous control environments that VGAIfO-SO is more sample efficient than other IfO algorithms at learning from video-only demonstrations and can sometimes even achieve performance close to the Generative Adversarial Imitation from Observation (GAIfO) algorithm that has privileged access to the demonstrator's proprioceptive state information.