{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/infogail-interpretable-imitation-learning","title":"InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations","arxiv_id":"1703.08840","date":"2017-03-26","proceeding":"NeurIPS 2017 12","authors":["Yunzhu Li","Jiaming Song","Stefano Ermon"],"abstract":"The goal of imitation learning is to mimic expert behavior without access to\nan explicit reward signal. Expert demonstrations provided by humans, however,\noften show significant variability due to latent factors that are typically not\nexplicitly modeled. In this paper, we propose a new algorithm that can infer\nthe latent structure of expert demonstrations in an unsupervised way. Our\nmethod, built on top of Generative Adversarial Imitation Learning, can not only\nimitate complex behaviors, but also learn interpretable and meaningful\nrepresentations of complex behavioral data, including visual demonstrations. In\nthe driving domain, we show that a model learned from human demonstrations is\nable to both accurately reproduce a variety of behaviors and accurately\nanticipate human actions using raw visual inputs. Compared with various\nbaselines, our method can better capture the latent structure underlying expert\ndemonstrations, often recovering semantically meaningful factors of variation\nin the data.","url_abs":"http://arxiv.org/abs/1703.08840v2","url_pdf":"http://arxiv.org/pdf/1703.08840v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"infogail-interpretable-imitation-learning","repo_url":"https://github.com/ermongroup/InfoGAIL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"infogail-interpretable-imitation-learning","repo_url":"https://github.com/YunzhuLi/InfoGAIL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"infogail-interpretable-imitation-learning","repo_url":"https://github.com/apbose/RLproject_AutonomousNavigation_Torcs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"infogail-interpretable-imitation-learning","repo_url":"https://github.com/sisl/ngsim_env","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"imitation-learning","task_name":"Imitation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.08840","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}