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LGMCTS: Language-Guided Monte-Carlo Tree Search for Executable Semantic Object Rearrangement
- Publication Year :
- 2023
-
Abstract
- 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.<br />Comment: Our code and supplementary materials are accessible at https://github.com/changhaonan/LG-MCTS
- Subjects :
- Computer Science - Robotics
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2309.15821
- Document Type :
- Working Paper