There has been an emerging paradigm shift from the era of “internet AI” to “embodied AI,”
where AI algorithms and agents no longer learn from datasets of images, videos or text …

Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI)
applications including computer vision, speech recognition, and robotics. While DNNs …

Semantic navigation is necessary to deploy mobile robots in uncontrolled environments
such as homes or hospitals. Many learning-based approaches have been proposed in …

We present the Habitat-Matterport 3D (HM3D) dataset. HM3D is a large-scale dataset of
1,000 building-scale 3D reconstructions from a diverse set of real-world locations. Each …

Vision-and-language navigation (VLN) aims to build autonomous visual agents that follow
instructions and navigate in real scenes. To remember previously visited locations and …

This work studies the problem of object goal navigation which involves navigating to an
instance of the given object category in unseen environments. End-to-end learning-based …

Following language instructions to navigate in unseen environments is a challenging
problem for autonomous embodied agents. The agent not only needs to ground languages …

We present Habitat, a platform for research in embodied artificial intelligence (AI). Habitat
enables training embodied agents (virtual robots) in highly efficient photorealistic 3D …

State-of-the-art approaches to ObjectGoal navigation (ObjectNav) rely on reinforcement
learning and typically require significant computational resources and time for learning. We …

Building home assistant robots has long been a goal for vision and robotics researchers. To
achieve this task, a simulated environment with physically realistic simulation, sufficient …