In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if
harnessed appropriately, may deliver the best of expectations over many application sectors …

This article describes a methodology for programming robots known as probabilistic
robotics. The probabilistic paradigm pays tribute to the inherent uncertainty in robot …

Partial observability is ubiquitous in applications of Reinforcement Learning (RL), in which
agents learn to make a sequence of decisions despite lacking complete information about …

Planning algorithms are impacting technical disciplines and industries around the world,
including robotics, computer-aided design, manufacturing, computer graphics, aerospace …

Trust is a critical factor for achieving the full potential of human-robot teams. Researchers
have theorized that people will more accurately trust an autonomous system, such as a …

To navigate reliably in indoor environments, a mobile robot must know where it is. Thus,
reliable position estimation is a key problem in mobile robotics. We believe that probabilistic …

Mobile robot localization is the problem of determining a robot's pose from sensor data. This
article presents a family of probabilistic localization algorithms known as Monte Carlo …

We describe an integrated strategy for planning, perception, state estimation and action in
complex mobile manipulation domains based on planning in the belief space of probability …

This paper presents a new algorithm for mobile robot localization, called Monte Carlo
Localization (MCL). MCL is a version of Markov localization, a family of probabilistic …

This article describes the software architecture of an autonomous, interactive tour-guide
robot. It presents a modular and distributed software architecture, which integrates …