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

arXiv: https://arxiv.org/abs/2502.17235 · HTML: https://mlsanity.com/paper/2502.17235/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 24 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Builds on (most cited first, 18 of 18)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Offline Reinforcement Learning with Implicit Q-Learning](https://mlsanity.com/paper/2110.06169/) (2021)
- [CLIPort: What and Where Pathways for Robotic Manipulation](https://mlsanity.com/paper/2109.12098/) (2021)
- [Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes](https://mlsanity.com/paper/2103.14127/) (2021)
- [DALL-E-Bot: Introducing Web-Scale Diffusion Models to Robotics](https://mlsanity.com/paper/2210.02438/) (2022)
- [StructFormer: Learning Spatial Structure for Language-Guided Semantic Rearrangement of Novel Objects](https://mlsanity.com/paper/2110.10189/) (2021)
- [Robotic Skill Acquisition via Instruction Augmentation with Vision-Language Models](https://mlsanity.com/paper/2211.11736/) (2022)
- [HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios](https://mlsanity.com/paper/2212.10428/) (2022)
- [StructDiffusion: Language-Guided Creation of Physically-Valid Structures using Unseen Objects](https://mlsanity.com/paper/2211.04604/) (2022)
- [IFOR: Iterative Flow Minimization for Robotic Object Rearrangement](https://mlsanity.com/paper/2202.00732/) (2022)
- [NeRP: Neural Rearrangement Planning for Unknown Objects](https://mlsanity.com/paper/2106.01352/) (2021)
- [My House, My Rules: Learning Tidying Preferences with Graph Neural Networks](https://mlsanity.com/paper/2111.03112/) (2021)
- [TarGF: Learning Target Gradient Field to Rearrange Objects without Explicit Goal Specification](https://mlsanity.com/paper/2209.00853/) (2022)
- [LGMCTS: Language-Guided Monte-Carlo Tree Search for Executable Semantic Object Rearrangement](https://mlsanity.com/paper/2309.15821/) (2023)
- [LVDiffusor: Distilling Functional Rearrangement Priors from Large Models into Diffusor](https://mlsanity.com/paper/2312.01474/) (2023)
- [SceneScore: Learning a Cost Function for Object Arrangement](https://mlsanity.com/paper/2311.08530/) (2023)

## Built on by (most cited first, 1 of 1)

- [Dynamic Buffers: Cost-Efficient Planning for Tabletop Rearrangement with Stacking](https://mlsanity.com/paper/2509.22828/) (2025)

## Abstract (arXiv)

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.