Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

Autonomous agents have long been a research focus in academic and industry
communities. Previous research often focuses on training agents with limited knowledge …

We study how vision-language models trained on Internet-scale data can be incorporated
directly into end-to-end robotic control to boost generalization and enable emergent …

In this paper, we introduce the BeaverTails dataset, aimed at fostering research on safety
alignment in large language models (LLMs). This dataset uniquely separates annotations of …

We study how vision-language models trained on Internet-scale data can be incorporated
directly into end-to-end robotic control to boost generalization and enable emergent …

Abstract We propose Text2Motion, a language-based planning framework enabling robots
to solve sequential manipulation tasks that require long-horizon reasoning. Given a natural …

Within the vast expanse of computerized language processing, a revolutionary entity known
as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to …

The recent surge in research interest in applying large language models (LLMs) to decision-
making tasks has flourished by leveraging the extensive world knowledge embedded in …

We observe that pre-trained large language models (LLMs) are capable of autoregressively
completing complex token sequences--from arbitrary ones procedurally generated by …

We propose a novel approach to multi-robot collaboration that harnesses the power of pre-
trained large language models (LLMs) for both high-level communication and low-level path …