LGMCTS: Language-Guided Monte-Carlo Tree Search for Executable Semantic Object Rearrangement
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866917795947610112 |
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| author | Chang, Haonan Gao, Kai Boyalakuntla, Kowndinya Lee, Alex Huang, Baichuan Kumar, Harish Udhaya Yu, Jinjin Boularias, Abdeslam |
| author_facet | Chang, Haonan Gao, Kai Boyalakuntla, Kowndinya Lee, Alex Huang, Baichuan Kumar, Harish Udhaya Yu, Jinjin Boularias, Abdeslam |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_15821 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | LGMCTS: Language-Guided Monte-Carlo Tree Search for Executable Semantic Object Rearrangement Chang, Haonan Gao, Kai Boyalakuntla, Kowndinya Lee, Alex Huang, Baichuan Kumar, Harish Udhaya Yu, Jinjin Boularias, Abdeslam Robotics 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. |
| title | LGMCTS: Language-Guided Monte-Carlo Tree Search for Executable Semantic Object Rearrangement |
| topic | Robotics |
| url | https://arxiv.org/abs/2309.15821 |