This month we going live again with webinar: Habitat: where Artificial Intelligent agents live by Oleksandr Maksymets, Research Engineer in Facebook AI Research
Imagine walking up to a home robot and asking “Hey robot — can you go check if my laptop is on my desk? And if so, bring it to me.” AI Habitat enables training of such embodied AI agents (virtual robots and egocentric assistants) in a highly photorealistic & efficient 3D simulator, before transferring the learned skills to reality. We will talk about state of the art in training intelligent agents’ domain using machine learning and how to scale a model to 30 years of house walking experience.
Oleksandr Maksymets is a research engineer at Facebook AI Research (FAIR) working on embodied agent navigation using deep learning. Co-author and maintainer of Open Source Habitat AI Framework from FAIR that brings community benchmarks to the field and supports simulation to reality transferability. Oleksandr was one of organizers of Embodied AI challenges and workshops for CVPR 2019/2020.
Oleksandr joined Facebook in October 2015 and worked on query understanding and document understanding at Facebook. He led a multilingual joint phrase and Facebook entities embeddings project that serves as a back-bone for multiple applications as entity linking and key phrase extractions.He is co-inventor on patent “Searching Online Social Networks Using Entity-based Embeddings”. He also implemented the mechanism of language models internationalization of Facebook Search.
From 2013 to 2015, Oleksandr was an assistant professor in the Cybernetics Department at Taras Shevchenko National University of Kyiv. In 2014 Oleksandr received his PhD in computer science from Taras Shevchenko National University of Kyiv, Ukraine.
Habitat: A platform for embodied ai research
M Savva, A Kadian, O Maksymets, Y Zhao, E Wijmans, B Jain, J Straub, ...
Proceedings of the IEEE International Conference on Computer Vision,
Embodied question-answering in photorealistic environments with point cloud perception
E Wijmans, S Datta, O Maksymets, A Das, G Gkioxari, S Lee, I Essa, ...
Proceedings of the IEEE Conference on Computer Vision and Pattern
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