Hierarchical Framework for Constrained Dual-Arm Cooperative Manipulation with Whole-Body Collision Avoidance

Abstract

Dual-arm robotic systems hold great potential for complex bimanual tasks that require intricate and coordinated manipulation, such as holding and transporting a tray with a cup of coffee while navigating through cluttered environments. However, these tasks pose significant challenges due to the inherent closed-chain constraints between the arms and the object, as well as the need for real-time collision avoidance, especially in real-world applications. To address these challenges, we introduce a hierarchical framework that combines learning-based planning with classical control theory to ensure whole-body collision avoidance movement while maintaining the kinematic relationship. In addition, we present a novel, efficient, and cost-free data generation method specifically designed for dual-arm cooperative tasks, overcoming the lack of sufficient training data. Extensive experiments in both simulation and real-world scenarios demonstrate that our approach improves the success rate by 26.3% compared to existing planning methods and by 54.7% compared to end-to-end methods. These results highlight the advantages of our method in whole-body collision avoidance and environmental adaptability, making it a promising solution for dual-arm cooperative tasks.