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Found 20 papers, 14 papers with code
Emergent Structures and Training Dynamics in Large Language Models
Large language models have achieved success on a number of downstream tasks, particularly in a few and zero-shot manner.
CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images
This task presents two challenges: (1) high-resolution CC images lack the captions necessary to train text-to-image generative models; (2) CC images are relatively scarce.
InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models
While diffusion models excel at generating high-quality samples, their latent variables typically lack semantic meaning and are not suitable for representation learning.
Galactic: Scaling End-to-End Reinforcement Learning for Rearrangement at 100k Steps-Per-Second
Specifically, a Fetch robot (equipped with a mobile base, 7DoF arm, RGBD camera, egomotion, and onboard sensing) is spawned in a home environment and asked to rearrange objects - by navigating to an object, picking it up, navigating to a target location, and then placing the object at the target location.
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
Habitat-Matterport 3D Semantics Dataset
The scale, quality, and diversity of object annotations far exceed those of prior datasets.
Data Governance in the Age of Large-Scale Data-Driven Language Technology
The recent emergence and adoption of Machine Learning technology, and specifically of Large Language Models, has drawn attention to the need for systematic and transparent management of language data.
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.
TöRF: Time-of-Flight Radiance Fields for Dynamic Scene View Synthesis
Neural networks can represent and accurately reconstruct radiance fields for static 3D scenes (e. g., NeRF).
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.
GaussiGAN: Controllable Image Synthesis with 3D Gaussians from Unposed Silhouettes
We present an algorithm that learns a coarse 3D representation of objects from unposed multi-view 2D mask supervision, then uses it to generate detailed mask and image texture.
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.
MatryODShka: Real-time 6DoF Video View Synthesis using Multi-Sphere Images
Our approach is to simultaneously learn depth and disocclusions via a multi-sphere image representation, which can be rendered with correct 6DoF disparity and motion parallax in VR.
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.
Generating Object Stamps
We present an algorithm to generate diverse foreground objects and composite them into background images using a GAN architecture.
Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance?
Second, we investigate the sim2real predictivity of Habitat-Sim for PointGoal navigation.
Learning Deep Parameterized Skills from Demonstration for Re-targetable Visuomotor Control
We demonstrate that our model trained on 33% of the possible goals is able to generalize to more than 90% of the targets in the scene for both simulation and robot experiments.
Improving Shape Deformation in Unsupervised Image-to-Image Translation
Unsupervised image-to-image translation techniques are able to map local texture between two domains, but they are typically unsuccessful when the domains require larger shape change.
