# LGMCTS: Language-Guided Monte-Carlo Tree Search for Executable Semantic Object Rearrangement

arXiv: https://arxiv.org/abs/2309.15821 · HTML: https://mlsanity.com/paper/2309.15821/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2023.

## Built on by (most cited first, 11 of 11)

- [Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI](https://mlsanity.com/paper/2407.06886/) (2024)
- [VSP: Assessing the dual challenges of perception and reasoning in spatial planning tasks for VLMs](https://mlsanity.com/paper/2407.01863/) (2024)
- [LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models](https://mlsanity.com/paper/2503.18971/) (2025)
- [A3VLM: Actionable Articulation-Aware Vision Language Model](https://mlsanity.com/paper/2406.07549/) (2024)
- [Improving Pre-Trained Vision-Language-Action Policies with Model-Based Search](https://mlsanity.com/paper/2508.12211/) (2025)
- [UniAff: A Unified Representation of Affordances for Tool Usage and Articulation with Vision-Language Models](https://mlsanity.com/paper/2409.20551/) (2024)
- [Tidiness Score-Guided Monte Carlo Tree Search for Visual Tabletop Rearrangement](https://mlsanity.com/paper/2502.17235/) (2025)
- [Decoupled Travel Planning with Behavior Forest](https://mlsanity.com/paper/2604.21354/) (2026)
- [Rule-Compliant Visual Spatial Planning for Multimodal Large Language Models](https://mlsanity.com/paper/2608.20237/) (2026)
- [Dynamic Buffers: Cost-Efficient Planning for Tabletop Rearrangement with Stacking](https://mlsanity.com/paper/2509.22828/) (2025)
- [ReRef-3D: A Benchmark for Spatial Referring Expression-Guided 3D Scene Rearrangement](https://mlsanity.com/paper/2608.16011/) (2026)

## Abstract (arXiv)

We introduce a novel approach to the executable semantic object rearrangement problem. In this challenge, a robot seeks to create an actionable plan that rearranges objects within a scene according to a pattern dictated by a natural language description. Unlike existing methods such as StructFormer and StructDiffusion, which tackle the issue in two steps by first generating poses and then leveraging a task planner for action plan formulation, our method concurrently addresses pose generation and action planning. We achieve this integration using a Language-Guided Monte-Carlo Tree Search (LGMCTS). Quantitative evaluations are provided on two simulation datasets, and complemented by qualitative tests with a real robot.