We survey applications of pretrained foundation models in robotics. Traditional deep
learning models in robotics are trained on small datasets tailored for specific tasks, which …

We present a modern formulation of Embodied Question Answering (EQA) as the task of
understanding an environment well enough to answer questions about it in natural …

Recent developments in foundation models, like Large Language Models (LLMs) and Vision-
Language Models (VLMs), trained on extensive data, facilitate flexible application across …

The proliferation of Large Language Models (LLMs) has s fueled a shift in robot learning
from automation towards general embodied Artificial Intelligence (AI). Adopting foundation …

Affordance grounding refers to the task of finding the area of an object with which one can
interact. It is a fundamental but challenging task as a successful solution requires the …

We show that large language models (LLMs) can be adapted to be generalizable policies
for embodied visual tasks. Our approach, called Large LAnguage model Reinforcement …

Remarkable progress has been made in recent years in the fields of vision, language, and
robotics. We now have vision models capable of recognizing objects based on language …

Large language models (LLMs) pre-trained on vast internet-scale data have showcased
remarkable capabilities across diverse domains. Recently, there has been escalating …

Abstract We contribute the Habitat Synthetic Scene Dataset a dataset of 211 high-quality 3D
scenes and use it to test navigation agent generalization to realistic 3D environments. Our …

Large Language Models (LLMs) have demonstrated the ability to perform semantic
reasoning, planning and write code for robotics tasks. However, most methods rely on pre …