RoboPARA: Dual-Arm Robot Planning with Parallel Allocation and Recomposition Across Tasks
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910044386230272 |
|---|---|
| author | Duan, Shiying Ren, Pei Jiang, Nanxiang Che, Zhengping Tang, Jian Fan, Zhaoxin Sun, Yifan Wu, Wenjun |
| author_facet | Duan, Shiying Ren, Pei Jiang, Nanxiang Che, Zhengping Tang, Jian Fan, Zhaoxin Sun, Yifan Wu, Wenjun |
| contents | Dual-arm robots play a crucial role in improving efficiency and flexibility in complex multitasking scenarios. While existing methods have achieved promising results in task planning, they often fail to fully optimize task parallelism, limiting the potential of dual-arm collaboration. To address this issue, we propose RoboPARA, a novel large language model (LLM)-driven framework for dual-arm task parallelism planning. RoboPARA employs a two-stage process: (1) Dependency Graph-based Planning Candidates Generation, which constructs directed acyclic graphs (DAGs) to model task dependencies and eliminate redundancy, and (2) Graph Re-Traversal-based Dual-Arm Parallel Planning, which optimizes DAG traversal to maximize parallelism while maintaining task coherence. In addition, we introduce the Cross-Scenario Dual-Arm Parallel Task dataset (X-DAPT dataset), the first dataset specifically designed to evaluate dual-arm task parallelism across diverse scenarios and difficulty levels. Extensive experiments demonstrate that RoboPARA significantly outperforms existing planning methods, achieving higher efficiency and reliability, particularly in complex task combinations. Our code is publicly available at https://github.com/AiDuanshiying/RoboPARA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06683 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RoboPARA: Dual-Arm Robot Planning with Parallel Allocation and Recomposition Across Tasks Duan, Shiying Ren, Pei Jiang, Nanxiang Che, Zhengping Tang, Jian Fan, Zhaoxin Sun, Yifan Wu, Wenjun Robotics Artificial Intelligence Dual-arm robots play a crucial role in improving efficiency and flexibility in complex multitasking scenarios. While existing methods have achieved promising results in task planning, they often fail to fully optimize task parallelism, limiting the potential of dual-arm collaboration. To address this issue, we propose RoboPARA, a novel large language model (LLM)-driven framework for dual-arm task parallelism planning. RoboPARA employs a two-stage process: (1) Dependency Graph-based Planning Candidates Generation, which constructs directed acyclic graphs (DAGs) to model task dependencies and eliminate redundancy, and (2) Graph Re-Traversal-based Dual-Arm Parallel Planning, which optimizes DAG traversal to maximize parallelism while maintaining task coherence. In addition, we introduce the Cross-Scenario Dual-Arm Parallel Task dataset (X-DAPT dataset), the first dataset specifically designed to evaluate dual-arm task parallelism across diverse scenarios and difficulty levels. Extensive experiments demonstrate that RoboPARA significantly outperforms existing planning methods, achieving higher efficiency and reliability, particularly in complex task combinations. Our code is publicly available at https://github.com/AiDuanshiying/RoboPARA. |
| title | RoboPARA: Dual-Arm Robot Planning with Parallel Allocation and Recomposition Across Tasks |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2506.06683 |