Abstract Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-
playing bots or solving sequential decision problems. The method relies on intelligent tree …

Language-conditioned robotic manipulation represents a cutting-edge area of research,
enabling seamless communication and cooperation between humans and robotic agents …

It has long been observed that for practically any computational problem that has been
intensely studied, different instances are best solved using different algorithms. This is …

In recent years, deep generative models have been shown to'imagine'convincing high-
dimensional observations such as images, audio, and even video, learning directly from raw …

We present a new algorithm for task and motion planning (TMP) and discuss the
requirements and abstractions necessary to obtain robust solutions for TMP in general. Our …

The task of algorithm selection involves choosing an algorithm from a set of algorithms on a
per-instance basis in order to exploit the varying performance of algorithms over a set of …

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

We present a new constraint-based framework for task and motion planning (TMP). Our
approach is extensible, probabilistically complete, and offers improved performance and …

The evaluation of artificial intelligence systems and components is crucial for the progress of
the discipline. In this paper we describe and critically assess the different ways AI systems …