The problem of planning for a robot that operates in environments containing a large
number of objects, taking actions to move itself through the world as well as to change the …

Autonomous robots are increasingly served in real-world unstructured human environments
with complex long-horizon tasks, such as restaurant serving and office delivery. Task and …

Transformers have revolutionized vision and natural language processing with their ability to
scale with large datasets. But in robotic manipulation, data is both limited and expensive …

Recent works have shown how the reasoning capabilities of Large Language Models
(LLMs) can be applied to domains beyond natural language processing, such as planning …

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

For effective human-robot interaction, robots need to understand, plan, and execute
complex, long-horizon tasks described by natural language. Recent advances in large …

Noisy sensing, imperfect control, and environment changes are defining characteristics of
many real-world robot tasks. The partially observable Markov decision process (POMDP) …

This book presents an overview of formal decision making methods for decentralized
cooperative systems. It is aimed at graduate students and researchers in the fields of …

How does language inform our downstream thinking? In particular, how do humans make
meaning from language--and how can we leverage a theory of linguistic meaning to build …

Neuro-symbolic AI is an emerging subfield of Artificial Intelligence that brings together two
hitherto distinct approaches.” Neuro” refers to the artificial neural networks prominent in …