{"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/uncertainty-aware-learning-from-demonstration","title":"Uncertainty-Aware Learning from Demonstration using Mixture Density Networks with Sampling-Free Variance Modeling","arxiv_id":"1709.02249","date":"2017-09-03","proceeding":null,"authors":["Sungjoon Choi","Kyungjae Lee","Sungbin Lim","Songhwai Oh"],"abstract":"In this paper, we propose an uncertainty-aware learning from demonstration\nmethod by presenting a novel uncertainty estimation method utilizing a mixture\ndensity network appropriate for modeling complex and noisy human behaviors. The\nproposed uncertainty acquisition can be done with a single forward path without\nMonte Carlo sampling and is suitable for real-time robotics applications. The\nproperties of the proposed uncertainty measure are analyzed through three\ndifferent synthetic examples, absence of data, heavy measurement noise, and\ncomposition of functions scenarios. We show that each case can be distinguished\nusing the proposed uncertainty measure and presented an uncertainty-aware\nlearn- ing from demonstration method of an autonomous driving using this\nproperty. The proposed uncertainty-aware learning from demonstration method\noutperforms other compared methods in terms of safety using a complex\nreal-world driving dataset.","url_abs":"http://arxiv.org/abs/1709.02249v2","url_pdf":"http://arxiv.org/pdf/1709.02249v2.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":"uncertainty-aware-learning-from-demonstration","repo_url":"https://github.com/taewankim1/uncertainty_deeplearning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1709.02249","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}