In recent years, the integration of large language models (LLMs) has revolutionized the field
of robotics, enabling robots to communicate, understand, and reason with human-like …

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 …

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

World models empower model-based agents to interactively explore, reason, and plan
within imagined environments for real-world decision-making. However, the high demand …

Representing robotic manipulation tasks as constraints that associate the robot and the
environment is a promising way to encode desired robot behaviors. However, it remains …

We give a model-based agent that builds a Python program representing its knowledge of
the world based on its interactions with the environment. The world model tries to explain its …

Scaling deep learning to massive, diverse internet data has yielded remarkably general
capabilities in visual and natural language understanding and generation. However, data …

Open-vocabulary generalization requires robotic systems to perform tasks involving complex
and diverse environments and task goals. While the recent advances in vision language …

Foundation models pre-trained on web-scale data are shown to encapsulate extensive
world knowledge beneficial for robotic manipulation in the form of task planning. However …

We present GR-2, a state-of-the-art generalist robot agent for versatile and generalizable
robot manipulation. GR-2 is first pre-trained on a vast number of Internet videos to capture …