For a long time, researchers have sought artificial intelligence (AI) that matches or exceeds
human intelligence. AI agents, which are artificial entities capable of sensing the …

Vision systems that see and reason about the compositional nature of visual scenes are
fundamental to understanding our world. The complex relations between objects and their …

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

For 3D object manipulation, methods that build an explicit 3D representation perform better
than those relying only on camera images. But using explicit 3D representations like voxels …

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

Robotic learning for navigation in unfamiliar environments needs to provide policies for both
task-oriented navigation (ie, reaching a goal that the robot has located), and task-agnostic …

General-purpose pre-trained models (" foundation models") have enabled practitioners to
produce generalizable solutions for individual machine learning problems with datasets that …

The human ability to learn, generalize, and control complex manipulation tasks through multi-
modality feedback suggests a unique capability, which we refer to as dexterity intelligence …

Learning provides a powerful tool for vision-based navigation, but the capabilities of
learning-based policies are constrained by limited training data. If we could combine data …

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 …