This paper presents a probabilistic framework combining heterogeneous, uncertain,
information such as object observations, shape, size, appearance of rooms and human input …

A long-standing goal of AI is to enable robots to plan in the face of uncertain and incomplete
information, and to handle task failure intelligently. This paper shows how to achieve this …

In this paper, we study the problem of active visual search (AVS) in large, unknown, or
partially known environments. We argue that by making use of uncertain semantics of the …

One of the central problems in computer vision is the detection of semantically important
objects and the estimation of their pose. Most of the work in object detection has been based …

In planning for deliberation or navigation in real-world robotic systems, one of the big
challenges is to cope with change. It lies in the nature of planning that it has to make …

This article describes REBA, a knowledge representation and reasoning architecture for
robots that is based on tightly-coupled transition diagrams of the domain at two different …

Robotic science missions in remote environments, such as deep ocean and outer space,
can involve studying phenomena that cannot directly be observed using on-board sensors …

While any grasp must satisfy the gras** stability criteria, good grasps depend on the
specific manipulation scenario: the object, its properties and functionalities, as well as the …

Deployment of robots in practical domains poses key knowledge representation and
reasoning challenges. Robots need to represent and reason with incomplete domain …

Finding objects in human environments requires autonomous mobile robots to reason about
potential object locations and to plan to perceive them accordingly. By using information …