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 …

Modern robots are increasingly capable of performing “basic” activities such as localization,
navigation, and motion planning. However, for a robot to be considered intelligent, we would …

We consider the problem of constructing abstract representations for planning in high-
dimensional, continuous environments. We assume an agent equipped with a collection of …

Generalized planning studies the representation, computation and evaluation of solutions
that are valid for multiple planning instances. These are topics studied since the early days …

Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential
decision problems under uncertainty as well as reinforcement learning problems. Written by …

People routinely infer the goals of others by observing their actions over time. Remarkably,
we can do so even when those actions lead to failure, enabling us to assist others when we …

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 …

FF-Replan was the winner of the 2004 International Probabilistic Planning Competition
(IPPC-04)(Younes & Littman 2004a) and was also the top performer on IPPC-06 domains …

Based on the unique problems and challenges in the disassembly scenario of waste electric
vehicle batteries (EVBs), we propose a knowledge-driven flexible human–robot hybrid …

In this article, we work towards the goal of developing agents that can learn to act in complex
worlds. We develop a probabilistic, relational planning rule representation that compactly …