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We investigate representations from pre-trained text-to-image diffusion models for control tasks and showcase competitive performance across a wide range of tasks.

We present a new embodied question answering (EQA) dataset with open vocabulary questions.

We conduct a study on using pre-trained visual representations (PVRs) to train robots for real-world tasks.

We present the largest and most comprehensive empirical study of visual foundation models for Embodied AI (EAI).

We propose a combined simulation and real-world benchmark on the problem of Open-Vocabulary Mobile Manipulation (OVMM).

We present a modular system that can perform well on the Instance ImageNav task in both simulation and the real world.

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

A last-mile navigation module that connects to prior policies, leading to improved image-goal navigation results in simulation and real-robot experiments.

In this work we propose OVRL, a two-stage representation learning strategy for visual navigation tasks in Embodied AI.

In this work we develop a gradient-based meta-learning algorithm for efficient, online continual learning, that is robust and scalable to real-world visual benchmarks.