🤖 AI Summary
Existing dual-arm manipulation approaches generally lack effective inter-arm interaction and dynamic role allocation mechanisms, hindering efficient collaboration. This work proposes the PA-BiCoop framework, which introduces—for the first time—a dynamic leader-follower role assignment mechanism within a single model. By employing a shared global feature encoder, role-specific decoders, and a follower-arm pose prediction module grounded in a relative coordinate system, the framework enables automatic role allocation and functional coordination. It further enhances inter-arm knowledge sharing and task synergy through heatmap-driven manipulability prediction for core tasks. Evaluated in the RLBench2 simulation environment, the method outperforms the current state-of-the-art by an average of 48%; in real-world tasks, it achieves performance gains exceeding 50%, demonstrating its effectiveness and strong generalization capability.
📝 Abstract
Bimanual manipulation is essential for advanced robotic systems because it offers higher efficiency and flexibility compared to single-arm configurations. However, existing approaches either lack inter-arm interaction or ignore the need for a dynamic division of labor, treating the arms as functionally equivalent. To address these limitations, this paper draws inspiration from human bimanual manipulation where one arm handles core operations and the other provides auxiliary support, and proposes PA-BiCoop, a new single-model bimanual cooperation framework with dynamic primary-auxiliary arm differentiation. PA-BiCoop categorizes robotic arms into primary and auxiliary arms with adaptively adjustable roles across task stages, employs two specialized decoders that share a global feature encoder: the primary decoder generates the primary arm's base-coordinate pose and core-task affordance heatmaps, and the auxiliary decoder outputs the auxiliary arm's relative pose in the primary arm's coordinate system. Moreover, we design a dynamic role assignment module to automatically map roles to left/right arms without manual pre-definition. This design facilitates inter-arm knowledge sharing and coordinated manipulation. Extensive experiments demonstrate that our PA-BiCoop achieves superior performance: it outperforms state-of-the-art baselines by 48% on average in RLBench2 simulation tasks and by over 50% on average in real world tasks, thereby verifying its effectiveness and advancement in bimanual manipulation.