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 …

This paper reports on an integrated inference and decision-making approach for
autonomous driving that models vehicle behavior for both our vehicle and nearby vehicles …

Robots in human environments will need to interact with a wide variety of articulated objects
such as cabinets, drawers, and dishwashers while assisting humans in performing day-to …

We tackle real-world long-horizon robot manipulation tasks through skill discovery. We
present a bottom-up approach to learning a library of reusable skills from unsegmented …

Existing autonomous robot navigation systems allow robots to move from one point to
another in a collision-free manner. However, when facing new environments, these systems …

While current autonomous navigation systems allow robots to successfully drive themselves
from one point to another in specific environments, they typically require extensive manual …

Robots operating in human environments must be capable of interacting with a wide variety
of articulated objects such as cabinets, refrigerators, and drawers. Existing approaches …

We propose an integrated prediction and planning system for autonomous driving which
uses rational inverse planning to recognise the goals of other vehicles. Goal recognition …

Detecting abrupt changes in data streams is crucial because they are often triggered by
events that have important consequences if left unattended. Quickest change-point detection …

We propose a method that efficiently learns distributions over articulation models directly
from depth images without the need to know articulation model categories a priori. By …