A common assumption when training embodied agents is that the impact of
...
Massive data corpora like WebText, Wikipedia, Conceptual Captions,
WebIm...
Training embodied agents in simulation has become mainstream for the emb...
Training effective embodied AI agents often involves manual reward
engin...
Embodied AI agents continue to become more capable every year with the a...
Massive datasets and high-capacity models have driven many recent
advanc...
We consider linear structural equation models with latent variables and
...
The last few years have witnessed substantial progress in the field of
e...
Embodied AI has shown promising results on an abundance of robotic tasks...
Contrastive language image pretraining (CLIP) encoders have been shown t...
We have observed significant progress in visual navigation for embodied
...
The domain of Embodied AI has recently witnessed substantial progress,
p...
While deep reinforcement learning (RL) promises freedom from hand-labele...
There has been a significant recent progress in the field of Embodied AI...
The domain of Embodied AI, in which agents learn to complete tasks throu...
Why do agents often obtain better reinforcement learning policies when
i...
Autonomous agents must learn to collaborate. It is not scalable to devel...
Visual recognition ecosystems (e.g. ImageNet, Pascal, COCO) have undenia...
We introduce Grounded Situation Recognition (GSR), a task that requires
...
The ubiquity of embodied gameplay, observed in a wide variety of animal
...
In this paper we address the problem of visual reaction: the task of
int...
A comprehensive and up-to-date analysis of Computer Science literature (...
Collaboration is a necessary skill to perform tasks that are beyond one
...
Directed graphical models specify noisy functional relationships among a...
The need to test whether two random vectors are independent has spawned ...
Gaussian latent tree models, or more generally, Gaussian latent forest m...