Habitat: A Platform for Embodied AI Research
Manolis Savva1,4*, Abhishek Kadian1*, Oleksandr Maksymets1*, Yili Zhao1, Erik Wijmans1,2,3, Bhavana Jain1, Julian Straub2, Jia Liu1, Vladlen Koltun5, Jitendra Malik1,6, Devi Parikh1,3, Dhruv Batra1,3 1Facebook AI Research, 2Facebook Reality Labs, 3Georgia Institute of Technology, 4Simon Fraser University, 5Intel Labs, 6UC Berkeley https://aihabitat.org
Abstract
We present Habitat, a platform for research in embodied artificial intelligence (AI). Habitat enables training embodied agents (virtual robots) in highly efficient photorealistic 3D simulation. Specifically, Habitat consists of: (i) Habitat-Sim: a flexible, high-performance 3D simulator with configurable agents, sensors, and generic 3D dataset handling. Habitat-Sim is fast – when rendering a scene from Matterport3D, it achieves several thousand frames per second (fps) running single-threaded, and can reach over $10@000$ fps multi-process on a single GPU. (ii) Habitat-API: a modular high-level library for end-to-end development of embodied AI algorithms – defining tasks (e.g. navigation, instruction following, question answering), configuring, training, and benchmarking embodied agents.
原文 arXiv:1904.01201;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1904.01201v2