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 …

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

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 …

Graph learning is naturally well suited for use in symbolic, object-centric planning due to its
ability to exploit relational structures exhibited in planning domains and to take as input …

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

In this paper, we describe the development of symbolic representations annotated on
human–robot dialogue data to make dimensions of meaning accessible to autonomous …

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 …

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 …

Large-scale generative language and vision-language models excel in in-context learning
for decision making. However, they require high-quality exemplar demonstrations to be …

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