Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within
the field of NLP, aimed at addressing limitations in existing frameworks while aligning with …

Pre-trained and frozen LLMs can effectively map simple scene re-arrangement instructions
to programs over a robot's visuomotor functions through appropriate few-shot example …

Embodied agents face significant challenges when tasked with performing actions in diverse
environments, particularly in generalizing across object types and executing suitable actions …

Large-scale generative language and vision-language models (LLMs and VLMs) excel in
few-shot in-context learning for decision making and instruction following. However, they …

Vision-and-Language Navigation (VLN) has gained increasing attention over recent years
and many approaches have emerged to advance their development. The remarkable …

Recent research on instructable agents has used memory-augmented Large Language
Models (LLMs) as task planners, a technique that retrieves language-program examples …

This paper introduces a novel method for determining the best room to place an object in, for
embodied scene rearrangement. While state-of-the-art approaches rely on large language …

Long-term memory is significant for agents, in which insights play a crucial role. However,
the emergence of irrelevant insight and the lack of general insight can greatly undermine the …

To collaborate effectively in physically situated tasks, robots must be able to ground
concepts in natural language to the physical objects in the environment as well as their own …

The growing demand for advanced AI necessitates the development of an intelligent agent
capable of perceiving, reasoning, acting, and communicating within an embodied …