While the exploration for embodied AI has spanned multiple decades, it remains a persistent
challenge to endow agents with human-level intelligence, including perception, learning …

Building general-purpose robots that can operate seamlessly, in any environment, with any
object, and utilizing various skills to complete diverse tasks has been a long-standing goal in …

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 …

The realization of universal robots is an ultimate goal of researchers. However, a key hurdle
in achieving this goal lies in the robots' ability to manipulate objects in their unstructured …

Recent advancements in large foundation models have remarkably enhanced our
understanding of sensory information in open-world environments. In leveraging the power …

This document serves as a position paper that outlines the authors' vision for a potential
pathway towards generalist robots. The purpose of this document is to share the excitement …

The domain of Embodied AI, in which agents learn to complete tasks through interaction with
their environment from egocentric observations, has experienced substantial growth with the …

Embodied AI has seen steady progress across a diverse set of independent tasks. While
these varied tasks have different end goals, the basic skills required to complete them …

Massive datasets and high-capacity models have driven many recent advancements in
computer vision and natural language understanding. This work presents a platform to …

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 …