There has been an emerging paradigm shift from the era of “internet AI” to “embodied AI,”
where AI algorithms and agents no longer learn from datasets of images, videos or text …

Embodied Artificial Intelligence (Embodied AI) is crucial for achieving Artificial General
Intelligence (AGI) and serves as a foundation for various applications that bridge cyberspace …

This technical report presents AutoGen, a new framework that enables development of LLM
applications using multiple agents that can converse with each other to solve tasks. AutoGen …

Large Language Models (LLMs) are becoming increasingly smart and autonomous,
targeting real-world pragmatic missions beyond traditional NLP tasks. As a result, there has …

We introduce PointOdyssey, a large-scale synthetic dataset, and data generation framework,
for the training and evaluation of long-term fine-grained tracking algorithms. Our goal is to …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

The study of zero-shot generalisation (ZSG) in deep Reinforcement Learning (RL) aims to
produce RL algorithms whose policies generalise well to novel unseen situations at …

Abstract We present the Habitat-Matterport 3D Semantics (HM3DSEM) dataset. HM3DSEM
is the largest dataset of 3D real-world spaces with densely annotated semantics that is …

A long-standing goal in computer vision is to capture, model, and realistically synthesize
human behavior. Specifically, by learning from data, our goal is to enable virtual humans to …

We introduce Housekeep, a benchmark to evaluate commonsense reasoning in the home
for embodied AI. In Housekeep, an embodied agent must tidy a house by rearranging …