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 …

The growing demand for robots able to act autonomously in complex scenarios has widely
accelerated the introduction of Reinforcement Learning (RL) in robots control applications …

Goal-conditioned policies for robotic navigation can be trained on large, unannotated
datasets, providing for good generalization to real-world settings. However, particularly in …

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 …

Massive datasets and high-capacity models have driven many recent advancements in
computer vision and natural language understanding. This work presents a platform to …

Understanding and reasoning about spatial relationships is crucial for Visual Question
Answering (VQA) and robotics. Vision Language Models (VLMs) have shown impressive …

Abstract We present Ego-Exo4D a diverse large-scale multimodal multiview video dataset
and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric …

Data is the driving force of machine learning, with the amount and quality of training data
often being more important for the performance of a system than architecture and training …

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

Contrastive language image pretraining (CLIP) encoders have been shown to be beneficial
for a range of visual tasks from classification and detection to captioning and image …