Learn from the Past: Language-conditioned Object Rearrangement with Large Language Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909525201649664 |
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| author | Cao, Guanqun Mckenna, Ryan Graf, Erich Oyekan, John |
| author_facet | Cao, Guanqun Mckenna, Ryan Graf, Erich Oyekan, John |
| contents | Object manipulation for rearrangement into a specific goal state is a significant task for collaborative robots. Accurately determining object placement is a key challenge, as misalignment can increase task complexity and the risk of collisions, affecting the efficiency of the rearrangement process. Most current methods heavily rely on pre-collected datasets to train the model for predicting the goal position. As a result, these methods are restricted to specific instructions, which limits their broader applicability and generalisation. In this paper, we propose a framework of flexible language-conditioned object rearrangement based on the Large Language Model (LLM). Our approach mimics human reasoning by making use of successful past experiences as a reference to infer the best strategies to achieve a current desired goal position. Based on LLM's strong natural language comprehension and inference ability, our method generalises to handle various everyday objects and free-form language instructions in a zero-shot manner. Experimental results demonstrate that our methods can effectively execute the robotic rearrangement tasks, even those involving long sequences of orders. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_18516 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learn from the Past: Language-conditioned Object Rearrangement with Large Language Models Cao, Guanqun Mckenna, Ryan Graf, Erich Oyekan, John Robotics Object manipulation for rearrangement into a specific goal state is a significant task for collaborative robots. Accurately determining object placement is a key challenge, as misalignment can increase task complexity and the risk of collisions, affecting the efficiency of the rearrangement process. Most current methods heavily rely on pre-collected datasets to train the model for predicting the goal position. As a result, these methods are restricted to specific instructions, which limits their broader applicability and generalisation. In this paper, we propose a framework of flexible language-conditioned object rearrangement based on the Large Language Model (LLM). Our approach mimics human reasoning by making use of successful past experiences as a reference to infer the best strategies to achieve a current desired goal position. Based on LLM's strong natural language comprehension and inference ability, our method generalises to handle various everyday objects and free-form language instructions in a zero-shot manner. Experimental results demonstrate that our methods can effectively execute the robotic rearrangement tasks, even those involving long sequences of orders. |
| title | Learn from the Past: Language-conditioned Object Rearrangement with Large Language Models |
| topic | Robotics |
| url | https://arxiv.org/abs/2501.18516 |