This review presents a comprehensive exploration of hybrid and ensemble deep learning
models within Natural Language Processing (NLP), shedding light on their transformative …

Large language models (LLMs) and Vision-Language Models (VLMs) have been proved to
excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be …

We present the largest and most comprehensive empirical study of pre-trained visual
representations (PVRs) or visual 'foundation models' for Embodied AI. First, we curate …

Semantic navigation is necessary to deploy mobile robots in uncontrolled environments
such as homes or hospitals. Many learning-based approaches have been proposed in …

We present a modern formulation of Embodied Question Answering (EQA) as the task of
understanding an environment well enough to answer questions about it in natural …

We propose CLIP-Fields, an implicit scene model that can be used for a variety of tasks,
such as segmentation, instance identification, semantic search over space, and view …

Humans are able to accurately reason in 3D by gathering multi-view observations of the
surrounding world. Inspired by this insight, we introduce a new large-scale benchmark for …

In the realm of computer vision and robotics embodied agents are expected to explore their
environment and carry out human instructions. This necessitates the ability to fully …

Abstract We study ObjectGoal Navigation--where a virtual robot situated in a new
environment is asked to navigate to an object. Prior work has shown that imitation learning …

Images are a convenient way to specify which particular object instance an embodied agent
should navigate to. Solving this task requires semantic visual reasoning and exploration of …