Abstract 3D scene graphs (3DSGs) are an emerging description; unifying symbolic,
topological, and metric scene representations. However, typical 3DSGs contain hundreds of …

Real-world planning problems often involve hundreds or even thousands of objects,
straining the limits of modern planners. In this work, we address this challenge by learning to …

Current academic vulnerability research predominantly focuses on identifying individual
bugs and exploits in programs and systems. However, this goes against the growing trend of …

The standard PDDL language for classical planning uses several first-order features, such
as schematic actions. Yet, most classical planners ground this first-order representation into …

The objective of top-k planning is to determine a set of k different plans with lowest cost for a
given planning task. In practice, such a set of best plans can be preferred to a single best …

Generalized planning accelerates classical planning by finding an algorithm-like policy that
solves multiple instances of a task. A generalized plan can be learned from a few training …

Recent research in classical planning has shown the importance of search techniques that
operate directly on the lifted representation of the problem, particularly in domains where the …

Task and motion planning problems in robotics combine symbolic planning over discrete
task variables with motion optimization over continuous state and action variables. Recent …

Matrix multiplication is a fundamental operation of linear algebra, with applications ranging
from quantum physics to artificial intelligence. Given its importance, enormous resources …

Classical planning instances are often represented using first-order logic; however, the
initial step for most classical planners is to transform the given instance into a propositional …