# Imitation Learning from Video by Leveraging Proprioception

arXiv: https://arxiv.org/abs/1905.09335 · HTML: https://mlsanity.com/paper/1905.09335/

## Key facts

- Cited by 23 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 19 of its 56 references that are in the map.
- First posted on arXiv 22 May 2019.

## Placed in

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

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Behavioral Cloning from Observation](https://mlsanity.com/paper/1805.01954/) (2018)
- [Learning Robust Rewards with Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1710.11248/) (2017)
- [An Algorithmic Perspective on Imitation Learning](https://mlsanity.com/paper/1811.06711/) (2018)
- [Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning](https://mlsanity.com/paper/1809.02925/) (2018)
- [Generative Adversarial Imitation from Observation](https://mlsanity.com/paper/1807.06158/) (2018)
- [Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation](https://mlsanity.com/paper/1707.03374/) (2017)
- [Third-Person Imitation Learning](https://mlsanity.com/paper/1703.01703/) (2017)
- [Imitating Latent Policies from Observation](https://mlsanity.com/paper/1805.07914/) (2018)
- [Playing hard exploration games by watching YouTube](https://mlsanity.com/paper/1805.11592/) (2018)
- [Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning](https://mlsanity.com/paper/1703.02949/) (2017)
- [Learning human behaviors from motion capture by adversarial imitation](https://mlsanity.com/paper/1707.02201/) (2017)
- [Adversarial Imitation via Variational Inverse Reinforcement Learning](https://mlsanity.com/paper/1809.06404/) (2018)
- [Learning Transferable Policies for Monocular Reactive MAV Control](https://mlsanity.com/paper/1608.00627/) (2016)
- [One-Shot Learning of Multi-Step Tasks from Observation via Activity Localization in Auxiliary Video](https://mlsanity.com/paper/1806.11244/) (2018)

## Built on by (most cited first, 23 of 23)

- [Recent Advances in Imitation Learning from Observation](https://mlsanity.com/paper/1905.13566/) (2019)
- [Imitation Learning: Progress, Taxonomies and Challenges](https://mlsanity.com/paper/2106.12177/) (2021)
- [Vision-based Manipulation from Single Human Video with Open-World Object Graphs](https://mlsanity.com/paper/2405.20321/) (2024)
- [Visuo-Tactile Transformers for Manipulation](https://mlsanity.com/paper/2210.00121/) (2022)
- [Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement](https://mlsanity.com/paper/1910.04417/) (2019)
- [VOILA: Visual-Observation-Only Imitation Learning for Autonomous Navigation](https://mlsanity.com/paper/2105.09371/) (2021)
- [K-VIL: Keypoints-based Visual Imitation Learning](https://mlsanity.com/paper/2209.03277/) (2022)
- [Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation](https://mlsanity.com/paper/2411.09891/) (2024)
- [A Survey of Imitation Learning Methods, Environments and Metrics](https://mlsanity.com/paper/2404.19456/) (2024)
- [Do You Need Proprioceptive States in Visuomotor Policies?](https://mlsanity.com/paper/2509.18644/) (2025)
- [Recent Advances in Leveraging Human Guidance for Sequential Decision-Making Tasks](https://mlsanity.com/paper/2107.05825/) (2021)
- [Adversarial Imitation Learning from Video using a State Observer](https://mlsanity.com/paper/2202.00243/) (2022)
- [SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models](https://mlsanity.com/paper/2306.10695/) (2023)
- [Learning Value Functions from Undirected State-only Experience](https://mlsanity.com/paper/2204.12458/) (2022)
- [Pre-trained Visual Dynamics Representations for Efficient Policy Learning](https://mlsanity.com/paper/2411.03169/) (2024)
- [Sample-efficient Adversarial Imitation Learning from Observation](https://mlsanity.com/paper/1906.07374/) (2019)
- [What You Say Is What You Show: Visual Narration Detection in Instructional Videos](https://mlsanity.com/paper/2301.02307/) (2023)
- [DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation](https://mlsanity.com/paper/2104.00163/) (2021)
- [NOLO: Navigate Only Look Once](https://mlsanity.com/paper/2408.01384/) (2024)
- [Genetic Imitation Learning by Reward Extrapolation](https://mlsanity.com/paper/2301.07182/) (2023)
- [Imitation from Observation With Bootstrapped Contrastive Learning](https://mlsanity.com/paper/2302.06540/) (2023)
- [GIFT: Geometry-Induced Functional Transfer for Category-level Object Manipulation](https://mlsanity.com/paper/2503.15371/) (2025)
- [Improving Robustness to Out-of-Distribution States in Imitation Learning via Deep Koopman-Boosted Diffusion Policy](https://mlsanity.com/paper/2511.00555/) (2025)

## Abstract (arXiv)

Classically, imitation learning algorithms have been developed for idealized situations, e.g., the demonstrations are often required to be collected in the exact same environment and usually include the demonstrator's actions. Recently, however, the research community has begun to address some of these shortcomings by offering algorithmic solutions that enable imitation learning from observation (IfO), e.g., learning to perform a task from visual demonstrations that may be in a different environment and do not include actions. Motivated by the fact that agents often also have access to their own internal states (i.e., proprioception), we propose and study an IfO algorithm that leverages this information in the policy learning process. The proposed architecture learns policies over proprioceptive state representations and compares the resulting trajectories visually to the demonstration data. We experimentally test the proposed technique on several MuJoCo domains and show that it outperforms other imitation from observation algorithms by a large margin.