Tidiness Score-Guided Monte Carlo Tree Search for Visual Tabletop Rearrangement

TL;DR

This paper introduces TSMCTS, a novel framework combining a tidiness score discriminator and Monte Carlo tree search to automate tabletop tidying using only RGB-D data, addressing dataset scarcity and goal specification challenges.

Contribution

The paper presents the TTU dataset and a vision-based discriminator for tidiness evaluation, enabling goal-free, diverse tidying trajectories via MCTS in real-world scenes.

Findings

Successfully applied to various environments including tables and bathrooms.

Discriminator accurately predicts tidiness across unseen configurations.

Demonstrated effective goal-free tidying with diverse arrangements.

Abstract

In this paper, we present the tidiness score-guided Monte Carlo tree search (TSMCTS), a novel framework designed to address the tabletop tidying up problem using only an RGB-D camera. We address two major problems for tabletop tidying up problem: (1) the lack of public datasets and benchmarks, and (2) the difficulty of specifying the goal configuration of unseen objects. We address the former by presenting the tabletop tidying up (TTU) dataset, a structured dataset collected in simulation. Using this dataset, we train a vision-based discriminator capable of predicting the tidiness score. This discriminator can consistently evaluate the degree of tidiness across unseen configurations, including real-world scenes. Addressing the second problem, we employ Monte Carlo tree search (MCTS) to find tidying trajectories without specifying explicit goals. Instead of providing specific goals, we demonstrate that our MCTS-based planner can find diverse tidied configurations using the tidiness score as a guidance. Consequently, we propose TSMCTS, which integrates a tidiness discriminator with an MCTS-based tidying planner to find optimal tidied arrangements. TSMCTS has successfully demonstrated its capability across various environments, including coffee tables, dining tables, office desks, and bathrooms. The TTU dataset is available at: https://github.com/rllab-snu/TTU-Dataset.