We introduce SceneDiffuser, a conditional generative model for 3D scene understanding.
SceneDiffuser provides a unified model for solving scene-conditioned generation …

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

We present BEHAVIOR-1K, a comprehensive simulation benchmark for human-centered
robotics. BEHAVIOR-1K includes two components, guided and motivated by the results of an …

We introduce BridgeData V2, a large and diverse dataset of robotic manipulation behaviors
designed to facilitate research in scalable robot learning. BridgeData V2 contains 53,896 …

In this work, we tackle the problem of learning universal robotic dexterous grasping from a
point cloud observation under a table-top setting. The goal is to grasp and lift up objects in …

We present HOI4D, a large-scale 4D egocentric dataset with rich annotations, to catalyze the
research of category-level human-object interaction. HOI4D consists of 2.4 M RGB-D …

We propose a novel, object-agnostic method for learning a universal policy for dexterous
object grasping from realistic point cloud observations and proprioceptive information under …

For years, researchers have been devoted to generalizable object perception and
manipulation, where cross-category generalizability is highly desired yet underexplored. In …

Legged robots have the potential to expand the reach of autonomy beyond paved roads. In
this work, we consider the difficult problem of locomotion on challenging terrains using a …

Generalizable manipulation skills, which can be composed to tackle long-horizon and
complex daily chores, are one of the cornerstones of Embodied AI. However, existing …