Meta has released the Habitat Platform, an AI training platform for embodied intelligence robots
Recently, AI giant Meta released the Habitat platform, a platform for the study of embodied artificial intelligence (AI), which can train embodied agents, that is, virtual robots, in efficient and realistic 3D simulations, through which the adaptability of real embodied intelligent robots in the physical world can be enhanced, and the ability of embodied intelligence to understand and interact in reality can be enhanced.
The Habitat-Lab platform is a modular advanced library for end-to-end experiments with embodied AI with the following key features:
(1) Define embodied AI tasks (e.g., navigation, instruction following, Q&A)
(2) Train agents, either by imitation or reinforcement learning, or not at all, as in the classic SensePlanAct pipeline, and benchmark their performance on defined tasks using standard metrics.
At present, the Habitat-Lab platform mainly consists of the following parts:
(1) Habitat-Sim: A flexible, high-performance 3D simulator with configurable agents, sensors, and general-purpose 3D dataset processing. One of the standout features of Habitat-Sim is its speed, which can achieve thousands of frames per second (fps) when rendering 3D scenes in 3D, and can reach more than 10,000 fps on a single GPU
Github address: Web link
- When rendering Matterport3D scenes, it can achieve thousands of frames per second (fps) when running with a single thread, and can achieve multi-processes in excess of 10,000 fps on a single GPU.
(2) Habitat-API: A modular high-level library for end-to-end development of embodied AI algorithms - defining tasks (e.g., navigation, instruction tracing, question answering), configuration, training, and benchmarking embodied agents.
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