Abstract We present the Habitat-Matterport 3D Semantics (HM3DSEM) dataset. HM3DSEM
is the largest dataset of 3D real-world spaces with densely annotated semantics that is …

We propose a novel type of map for visual navigation, a renderable neural radiance map
(RNR-Map), which is designed to contain the overall visual information of a 3D environment …

We address a practical yet challenging problem of training robot agents to navigate in an
environment following a path described by some language instructions. The instructions …

We consider the problem of Vision-and-Language Navigation (VLN). The majority of current
methods for VLN are trained end-to-end using either unstructured memory such as LSTM, or …

Being able to perceive the semantics and the spatial structure of the environment is
essential for visual navigation of a household robot. However, most existing works only …

Object goal navigation (ObjectNav) in unseen environments is a fundamental task for
Embodied AI. Agents in existing works learn ObjectNav policies based on 2D maps, scene …

The Embodied AI community has recently made significant strides in visual navigation tasks
exploring targets from 3D coordinates objects language description and images. However …

We address the problem of efficient 3-D exploration in indoor environments for micro aerial
vehicles with limited sensing capabilities and payload/power constraints. We develop an …

We consider the problems of exploration and pointgoal navigation in previously unseen
environments, where the spatial complexity of indoor scenes and partial observability …

Abstract Efficient ObjectGoal navigation (ObjectNav) in novel environments requires an
understanding of the spatial and semantic regularities in environment layouts. In this work …