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 …

This article is about deep learning (DL) and deep reinforcement learning (DRL) works
applied to robotics. Both tools have been shown to be successful in delivering data-driven …

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

Navigation in unfamiliar environments presents a major challenge for robots: while mapping
and planning techniques can be used to build up a representation of the world, quickly …

The ability to predict future visual observations conditioned on past observations and motor
commands can enable embodied agents to plan solutions to a variety of tasks in complex …

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

Mobile robot navigation is typically regarded as a geometric problem, in which the robot's
objective is to perceive the geometry of the environment in order to plan collision-free paths …

We introduce BEHAVIOR, a benchmark for embodied AI with 100 activities in simulation,
spanning a range of everyday household chores such as cleaning, maintenance, and food …

Social navigation is the capability of an autonomous agent, such as a robot, to navigate in a
“socially compliant” manner in the presence of other intelligent agents such as humans. With …

We present iGibson 1.0, a novel simulation environment to develop robotic solutions for
interactive tasks in large-scale realistic scenes. Our environment contains 15 fully interactive …