Habitat Simulator

Habitat simulators are virtual environments used to train and test embodied AI agents, focusing on tasks like navigation, object manipulation, and scene understanding in realistic 3D indoor spaces. Current research emphasizes developing robust and efficient algorithms for navigation, often employing neural networks and graph-based methods to handle complex scenes and long-horizon planning, while also exploring the use of pre-trained models like CLIP for improved semantic understanding. These simulators, coupled with large-scale datasets like Habitat-Matterport 3D Semantics, are accelerating progress in embodied AI by providing standardized benchmarks and facilitating the development of more capable and generalizable agents for robotics and other applications.