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.
@article{arxiv.2309.15821,
title = {LGMCTS: Language-Guided Monte-Carlo Tree Search for Executable Semantic Object Rearrangement},
author = {Haonan Chang and Kai Gao and Kowndinya Boyalakuntla and Alex Lee and Baichuan Huang and Harish Udhaya Kumar and Jinjin Yu and Abdeslam Boularias},
journal= {arXiv preprint arXiv:2309.15821},
year = {2024}
}
Comments
Our code and supplementary materials are accessible at https://github.com/changhaonan/LG-MCTS