Large language models (LLMs) have undergone significant expansion and have been
increasingly integrated across various domains. Notably, in the realm of robot task planning …

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

Large language models (LLMs) are shown to possess a wealth of actionable knowledge that
can be extracted for robot manipulation in the form of reasoning and planning. Despite the …

How to efficiently transform large language models (LLMs) into instruction followers is
recently a popular research direction, while training LLM for multi-modal reasoning remains …

For a robot to personalize physical assistance effectively, it must learn user preferences that
can be generally reapplied to future scenarios. In this work, we investigate personalization of …

Large language models (LLMs) have shown remarkable reasoning capabilities, especially
when prompted to generate intermediate reasoning steps (eg, Chain-of-Thought, CoT) …

Large Language Models (LLMs) have excelled as high-level semantic planners for
sequential decision-making tasks. However, harnessing them to learn complex low-level …

Large language models (LLMs) have demonstrated remarkable zero-shot generalization
abilities: state-of-the-art chatbots can provide plausible answers to many common questions …

We investigate the challenge of task planning for multi-task embodied agents in open-world
environments. Two main difficulties are identified: 1) executing plans in an open-world …

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 …