Taskography: Evaluating Robot Task Planning over Large 3D Scene Graphs

Abstract

3D scene graphs (3DSGs) are an emerging description; unifying symbolic, topological, and metric scene representations. However, typical 3DSGs contain hundreds of objects and symbols even for small environments; rendering task planning on the \emph{full} graph impractical. We construct \textbf{Taskography}, the first large-scale robotic task planning benchmark over 3DSGs. While most benchmarking efforts in this area focus on \emph{vision-based planning}, we systematically study \emph{symbolic} planning, to decouple planning performance from visual representation learning. We observe that, among existing methods, neither classical nor learning-based planners are capable of real-time planning over \emph{full} 3DSGs. Enabling real-time planning demands progress on \emph{both} (a) sparsifying 3DSGs for tractable planning and (b) designing planners that better exploit 3DSG hierarchies. Towards the former goal, we propose \textbf{Scrub}, a task-conditioned 3DSG sparsification method; enabling classical planners to match (and surpass) state-of-the-art learning-based planners. Towards the latter goal, we propose \textbf{Seek}, a procedure enabling learning-based planners to exploit 3DSG structure, reducing the number of replanning queries required by current best approaches by an order of magnitude. We will open-source all code and baselines to spur further research along the intersections of robot task planning, learning and 3DSGs.