RoboPARA: Dual-Arm Robot Planning with Parallel Allocation and Recomposition Across Tasks

Abstract: 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 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 on the X-DAPT dataset demonstrate that RoboPARA significantly outperforms existing methods, achieving higher efficiency and reliability, particularly in complex task combinations. The code and dataset will be released upon acceptance.

• The paper introduces RoboPARA, which employs a two-stage LLM-driven framework to generate and optimize dependency graphs for efficient dual-arm task execution.
• It achieves a 30-50% reduction in execution time and over 4.5 times more parallel steps, demonstrating significant efficiency gains.
• The work also introduces the X-DAPT dataset as a novel benchmark for dual-arm planning, promoting further research in task parallelism.

Overview of RoboPARA: Dual-Arm Robot Planning

The paper presents RoboPARA, a novel framework for dual-arm robot task planning aimed at optimizing task parallelism in complex multitasking scenarios. The significance of the proposed framework lies in its ability to enhance the potential of dual-arm collaboration through efficient task scheduling and execution. RoboPARA implements a two-stage LLM-driven approach, which includes Dependency Graph-based Planning Candidates Generation and Graph Re-Traversal-based Dual-Arm Parallel Planning. Additionally, the authors introduce the Cross-Scenario Dual-Arm Parallel Task dataset (X-DAPT), tailored for evaluating the parallelism of dual-arm task planning.

Methodological Approach

RoboPARA features a distinct two-stage architecture:

1. Dependency Graph-based Planning Candidates Generation: This stage utilizes an LLM-driven mechanism to generate task dependencies. Given user instructions, RoboPARA retrieves relevant procedural knowledge and constructs a directed acyclic graph (DAG) representing task dependencies. The approach involves iterative corrections to eliminate redundancies and errors, ensuring that the DAG accurately models the dependencies required for efficient task execution.
2. Graph Re-Traversal-based Dual-Arm Parallel Planning: In this stage, RoboPARA analyzes and optimizes DAG traversal to fully exploit dual-arm collaboration, ensuring task coherence. It employs scheduling algorithms to maximize parallel execution while adhering to constraints like arm availability and task dependencies. This stage integrates deadlock prevention methods and arm assignment strategies, facilitating dynamic task execution.

Strong Numerical Results

RoboPARA demonstrates substantial efficiency gains in dual-arm task execution, achieving a 30\% to 50\% reduction in execution time compared to existing methods. Empirical evidence from experiments on the X-DAPT dataset indicates that RoboPARA significantly surpasses existing planning strategies, particularly in complex task combinations. The framework yields over 4.5 times more parallel and collaborative steps on average, and enhances task reliability with a 34\% higher success rate compared to other methods.

Implications and Future Directions

The implications of RoboPARA extend both practically and theoretically. Practically, it offers a robust solution for enhancing the efficiency and reliability of dual-arm robotic systems in diverse application scenarios, from industrial settings to domestic environments. Theoretically, RoboPARA sets a benchmark for evaluating dual-arm planning frameworks, promoting further research into optimizing task parallelism and scheduling algorithms.

In future developments, efforts may focus on scaling RoboPARA across a broader range of tasks and environments, exploring adaptive DAG generation mechanisms, and integrating advancements in LLM to enhance its generalization capabilities. Additionally, addressing challenges related to scalability and accuracy in DAG representations could unlock further potential for dual-arm robots to achieve human-like collaborative efficiency.

In conclusion, RoboPARA represents a significant advancement in dual-arm robot task planning, presenting a framework that not only improves execution efficiency but also enhances task reliability across complex scenarios. The introduction of the X-DAPT dataset provides a valuable resource for further exploration and benchmarking within this domain.