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Found 11 papers, 5 papers with code
What do we learn from a large-scale study of pre-trained visual representations in sim and real environments?
We present a large empirical investigation on the use of pre-trained visual representations (PVRs) for training downstream policies that execute real-world tasks.
HomeRobot: Open-Vocabulary Mobile Manipulation
HomeRobot (noun): An affordable compliant robot that navigates homes and manipulates a wide range of objects in order to complete everyday tasks.
Navigating to Objects Specified by Images
Our modular method solves sub-tasks of exploration, goal instance re-identification, goal localization, and local navigation.
Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?
Contrary to inferences from prior work, we find that scaling dataset size and diversity does not improve performance universally (but does so on average).
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.
Last-Mile Embodied Visual Navigation
Realistic long-horizon tasks like image-goal navigation involve exploratory and exploitative phases.
Habitat-Matterport 3D Semantics Dataset
The scale, quality, and diversity of object annotations far exceed those of prior datasets.
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.
Look-ahead Meta Learning for Continual Learning
The continual learning problem involves training models with limited capacity to perform well on a set of an unknown number of sequentially arriving tasks.
La-MAML: Look-ahead Meta Learning for Continual Learning
The continual learning problem involves training models with limited capacity to perform well on a set of an unknown number of sequentially arriving tasks.
Learning to Prevent Monocular SLAM Failure using Reinforcement Learning
Monocular SLAM refers to using a single camera to estimate robot ego motion while building a map of the environment.
Robotics
