# InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations

arXiv: https://arxiv.org/abs/1703.08840 · HTML: https://mlsanity.com/paper/1703.08840/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2017.

## Built on by (most cited first, 11 of 11)

- [Reinforcement and Imitation Learning for Diverse Visuomotor Skills](https://mlsanity.com/paper/1802.09564/) (2018)
- [Learning a Prior over Intent via Meta-Inverse Reinforcement Learning](https://mlsanity.com/paper/1805.12573/) (2018)
- [Sample-Efficient Imitation Learning via Generative Adversarial Nets](https://mlsanity.com/paper/1809.02064/) (2018)
- [Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions](https://mlsanity.com/paper/2402.13777/) (2024)
- [Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation](https://mlsanity.com/paper/2105.09637/) (2021)
- [OLLIE: Imitation Learning from Offline Pretraining to Online Finetuning](https://mlsanity.com/paper/2405.17477/) (2024)
- [Learning Temporal Strategic Relationships using Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1805.04969/) (2018)
- [Imitation Learning for Generalizable Self-driving Policy with Sim-to-real Transfer](https://mlsanity.com/paper/2206.10797/) (2022)
- [Interpretable and Flexible Target-Conditioned Neural Planners For Autonomous Vehicles](https://mlsanity.com/paper/2309.13485/) (2023)
- [Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigation](https://mlsanity.com/paper/2407.06317/) (2024)
- [Preventing Imitation Learning with Adversarial Policy Ensembles](https://mlsanity.com/paper/2002.01059/) (2020)

## Abstract (arXiv)

The goal of imitation learning is to mimic expert behavior without access to an explicit reward signal. Expert demonstrations provided by humans, however, often show significant variability due to latent factors that are typically not explicitly modeled. In this paper, we propose a new algorithm that can infer the latent structure of expert demonstrations in an unsupervised way. Our method, built on top of Generative Adversarial Imitation Learning, can not only imitate complex behaviors, but also learn interpretable and meaningful representations of complex behavioral data, including visual demonstrations. In the driving domain, we show that a model learned from human demonstrations is able to both accurately reproduce a variety of behaviors and accurately anticipate human actions using raw visual inputs. Compared with various baselines, our method can better capture the latent structure underlying expert demonstrations, often recovering semantically meaningful factors of variation in the data.