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Found 13 papers, 7 papers with code
OVRL-V2: A simple state-of-art baseline for ImageNav and ObjectNav
We present a single neural network architecture composed of task-agnostic components (ViTs, convolutions, and LSTMs) that achieves state-of-art results on both the ImageNav ("go to location in <this picture>") and ObjectNav ("find a chair") tasks without any task-specific modules like object detection, segmentation, mapping, or planning modules.
Retrospectives on the Embodied AI Workshop
We present a retrospective on the state of Embodied AI research.
Is Mapping Necessary for Realistic PointGoal Navigation?
However, for PointNav in a realistic setting (RGB-D and actuation noise, no GPS+Compass), this is an open question; one we tackle in this paper.
Offline Visual Representation Learning for Embodied Navigation
In this paper, we show that an alternative 2-stage strategy is far more effective: (1) offline pretraining of visual representations with self-supervised learning (SSL) using large-scale pre-rendered images of indoor environments (Omnidata), and (2) online finetuning of visuomotor representations on specific tasks with image augmentations under long learning schedules.
Waypoint Models for Instruction-guided Navigation in Continuous Environments
Little inquiry has explicitly addressed the role of action spaces in language-guided visual navigation -- either in terms of its effect on navigation success or the efficiency with which a robotic agent could execute the resulting trajectory.
Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI
When compared to existing photorealistic 3D datasets such as Replica, MP3D, Gibson, and ScanNet, images rendered from HM3D have 20 - 85% higher visual fidelity w. r. t.
Habitat 2.0: Training Home Assistants to Rearrange their Habitat
We introduce Habitat 2. 0 (H2. 0), a simulation platform for training virtual robots in interactive 3D environments and complex physics-enabled scenarios.
Memory-Augmented Reinforcement Learning for Image-Goal Navigation
In this work, we present a memory-augmented approach for image-goal navigation.
THDA: Treasure Hunt Data Augmentation for Semantic Navigation
We show that this is a natural consequence of optimizing for the task metric (which in fact penalizes exploration), is enabled by powerful observation encoders, and is possible due to the finite set of training environment configurations.
Integrating Egocentric Localization for More Realistic Point-Goal Navigation Agents
This enables a seamless adaption to changing dynamics (a different robot or floor type) by simply re-calibrating the visual odometry model -- circumventing the expense of re-training of the navigation policy.
Ranked #5 on
Robot Navigation
on Habitat 2020 Point Nav test-std
ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects
In particular, the agent is initialized at a random location and pose in an environment and asked to find an instance of an object category, e. g., find a chair, by navigating to it.
Embodied Question Answering in Photorealistic Environments with Point Cloud Perception
To help bridge the gap between internet vision-style problems and the goal of vision for embodied perception we instantiate a large-scale navigation task -- Embodied Question Answering [1] in photo-realistic environments (Matterport 3D).
Habitat: A Platform for Embodied AI Research
We present Habitat, a platform for research in embodied artificial intelligence (AI).
Ranked #2 on
PointGoal Navigation
on Gibson PointGoal Navigation
