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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.09022 |
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| _version_ | 1866910856560771072 |
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| author | Wang, Yongdong Xiao, Runze Kasahara, Jun Younes Louhi Yajima, Ryosuke Nagatani, Keiji Yamashita, Atsushi Asama, Hajime |
| author_facet | Wang, Yongdong Xiao, Runze Kasahara, Jun Younes Louhi Yajima, Ryosuke Nagatani, Keiji Yamashita, Atsushi Asama, Hajime |
| contents | Large Language Models (LLMs) have demonstrated promising reasoning capabilities in robotics; however, their application in multi-robot systems remains limited, particularly in handling task dependencies. This paper introduces DART-LLM, a novel framework that employs Directed Acyclic Graphs (DAGs) to model task dependencies, enabling the decomposition of natural language instructions into well-coordinated subtasks for multi-robot execution. DART-LLM comprises four key components: a Question-Answering (QA) LLM module for dependency-aware task decomposition, a Breakdown Function module for robot assignment, an Actuation module for execution, and a Vision-Language Model (VLM)-based object detector for environmental perception, achieving end-to-end task execution. Experimental results across three task complexity levels demonstrate that DART-LLM achieves state-of-the-art performance, significantly outperforming the baseline across all evaluation metrics. Among the tested models, DeepSeek-r1-671B achieves the highest success rate, whereas Llama-3.1-8B exhibits superior response time reliability. Ablation studies further confirm that explicit dependency modeling notably enhances the performance of smaller models, facilitating efficient deployment on resource-constrained platforms. Please refer to the project website https://wyd0817.github.io/project-dart-llm/ for videos and code. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_09022 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DART-LLM: Dependency-Aware Multi-Robot Task Decomposition and Execution using Large Language Models Wang, Yongdong Xiao, Runze Kasahara, Jun Younes Louhi Yajima, Ryosuke Nagatani, Keiji Yamashita, Atsushi Asama, Hajime Robotics Large Language Models (LLMs) have demonstrated promising reasoning capabilities in robotics; however, their application in multi-robot systems remains limited, particularly in handling task dependencies. This paper introduces DART-LLM, a novel framework that employs Directed Acyclic Graphs (DAGs) to model task dependencies, enabling the decomposition of natural language instructions into well-coordinated subtasks for multi-robot execution. DART-LLM comprises four key components: a Question-Answering (QA) LLM module for dependency-aware task decomposition, a Breakdown Function module for robot assignment, an Actuation module for execution, and a Vision-Language Model (VLM)-based object detector for environmental perception, achieving end-to-end task execution. Experimental results across three task complexity levels demonstrate that DART-LLM achieves state-of-the-art performance, significantly outperforming the baseline across all evaluation metrics. Among the tested models, DeepSeek-r1-671B achieves the highest success rate, whereas Llama-3.1-8B exhibits superior response time reliability. Ablation studies further confirm that explicit dependency modeling notably enhances the performance of smaller models, facilitating efficient deployment on resource-constrained platforms. Please refer to the project website https://wyd0817.github.io/project-dart-llm/ for videos and code. |
| title | DART-LLM: Dependency-Aware Multi-Robot Task Decomposition and Execution using Large Language Models |
| topic | Robotics |
| url | https://arxiv.org/abs/2411.09022 |