Recently human–robot collaboration (HRC) has emerged as a promising paradigm for mass
personalization in manufacturing owing to the potential to fully exploit the strength of human …

A key challenge in intelligent robotics is creating robots that are capable of directly
interacting with the world around them to achieve their goals. The last decade has seen …

Building a robot that can understand and learn to interact by watching humans has inspired
several vision problems. However, despite some successful results on static datasets, it …

Enabling robotic manipulation that generalizes to out-of-distribution scenes is a crucial step
toward the open-world embodied intelligence. For human beings, this ability is rooted in the …

We describe a framework for research and evaluation in Embodied AI. Our proposal is
based on a canonical task: Rearrangement. A standard task can focus the development of …

How human interact with objects depends on the functional roles of the target objects, which
introduces the problem of affordance-aware hand-object interaction. It requires a large …

For robots to navigate and interact more richly with the world around them, they will likely
require a deeper understanding of the world in which they operate. In robotics and related …

We propose to forecast future hand-object interactions given an egocentric video. Instead of
predicting action labels or pixels, we directly predict the hand motion trajectory and the …

We propose AffordanceNet, a new deep learning approach to simultaneously detect multiple
objects and their affordances from RGB images. Our AffordanceNet has two branches: an …

Humans excel at acquiring knowledge through observation. For example, we can learn to
use new tools by watching demonstrations. This skill is fundamental for intelligent systems to …