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 …

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

The emergence of Large Language Models (LLMs) has improved the prospects for robotic
tasks. However, existing benchmarks are still limited to single tasks with limited …

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 …

Scaling laws have driven remarkable progress across machine learning domains like
language modeling and computer vision. However, the exploration of scaling laws in …

We study the problem of offline pre-training and online fine-tuning for reinforcement learning
from high-dimensional observations in the context of realistic robot tasks. Recent offline …

We present RoboGen, a generative robotic agent that automatically learns diverse robotic
skills at scale via generative simulation. RoboGen leverages the latest advancements in …

Recent progress in vision language foundation models has shown their ability to understand
multimodal data and resolve complicated vision language tasks, including robotics …