Language-Guided Long Horizon Manipulation with LLM-based Planning and Visual Perception
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916930130018304 |
|---|---|
| author | Zhou, Changshi Xu, Haichuan Gu, Ningquan Wang, Zhipeng Cheng, Bin Zhang, Pengpeng Dong, Yanchao Hayashibe, Mitsuhiro Zhou, Yanmin He, Bin |
| author_facet | Zhou, Changshi Xu, Haichuan Gu, Ningquan Wang, Zhipeng Cheng, Bin Zhang, Pengpeng Dong, Yanchao Hayashibe, Mitsuhiro Zhou, Yanmin He, Bin |
| contents | Language-guided long-horizon manipulation of deformable objects presents significant challenges due to high degrees of freedom, complex dynamics, and the need for accurate vision-language grounding. In this work, we focus on multi-step cloth folding, a representative deformable-object manipulation task that requires both structured long-horizon planning and fine-grained visual perception. To this end, we propose a unified framework that integrates a Large Language Model (LLM)-based planner, a Vision-Language Model (VLM)-based perception system, and a task execution module. Specifically, the LLM-based planner decomposes high-level language instructions into low-level action primitives, bridging the semantic-execution gap, aligning perception with action, and enhancing generalization. The VLM-based perception module employs a SigLIP2-driven architecture with a bidirectional cross-attention fusion mechanism and weight-decomposed low-rank adaptation (DoRA) fine-tuning to achieve language-conditioned fine-grained visual grounding. Experiments in both simulation and real-world settings demonstrate the method's effectiveness. In simulation, it outperforms state-of-the-art baselines by 2.23, 1.87, and 33.3 on seen instructions, unseen instructions, and unseen tasks, respectively. On a real robot, it robustly executes multi-step folding sequences from language instructions across diverse cloth materials and configurations, demonstrating strong generalization in practical scenarios. Project page: https://language-guided.netlify.app/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02324 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Language-Guided Long Horizon Manipulation with LLM-based Planning and Visual Perception Zhou, Changshi Xu, Haichuan Gu, Ningquan Wang, Zhipeng Cheng, Bin Zhang, Pengpeng Dong, Yanchao Hayashibe, Mitsuhiro Zhou, Yanmin He, Bin Robotics Language-guided long-horizon manipulation of deformable objects presents significant challenges due to high degrees of freedom, complex dynamics, and the need for accurate vision-language grounding. In this work, we focus on multi-step cloth folding, a representative deformable-object manipulation task that requires both structured long-horizon planning and fine-grained visual perception. To this end, we propose a unified framework that integrates a Large Language Model (LLM)-based planner, a Vision-Language Model (VLM)-based perception system, and a task execution module. Specifically, the LLM-based planner decomposes high-level language instructions into low-level action primitives, bridging the semantic-execution gap, aligning perception with action, and enhancing generalization. The VLM-based perception module employs a SigLIP2-driven architecture with a bidirectional cross-attention fusion mechanism and weight-decomposed low-rank adaptation (DoRA) fine-tuning to achieve language-conditioned fine-grained visual grounding. Experiments in both simulation and real-world settings demonstrate the method's effectiveness. In simulation, it outperforms state-of-the-art baselines by 2.23, 1.87, and 33.3 on seen instructions, unseen instructions, and unseen tasks, respectively. On a real robot, it robustly executes multi-step folding sequences from language instructions across diverse cloth materials and configurations, demonstrating strong generalization in practical scenarios. Project page: https://language-guided.netlify.app/ |
| title | Language-Guided Long Horizon Manipulation with LLM-based Planning and Visual Perception |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.02324 |