用图神经网络让机器人智能规划多部件装配顺序。
Subassembly to Full Assembly: Effective Assembly Sequence Planning through Graph-based Reinforcement Learning
- 基于图注意力网络的强化学习方法，建模零件间装配关系。
- 延迟奖励机制显著提升复杂装配任务的成功率。
- 适用于工业机器人真实场景的装配规划，可扩展性强。
本文提出一种名为子组件到整体（Subassembly to Assembly, S2A）的装配序列规划框架，旨在使机器人操作臂通过物体操作动作完成指定结构的多部件装配。主要技术挑战在于，随着零件数量增加，可行装配序列的搜索空间呈指数级增长。为此，我们引入一种基于图的强化学习方法，利用图注意力网络并结合延迟奖励分配策略：仅当某装配动作有助于任务最终成功时才给予奖励。通过物理仿真验证，该框架在多个基线方法上表现更优，凸显了延迟奖励机制的重要性。此外，还展示了该框架在真实机器人装配场景中的可行性。
原文摘要 · Abstract (English)
This paper proposes an assembly sequence planning framework, named Subassembly to Assembly (S2A). The framework is designed to enable a robotic manipulator to assemble multiple parts in a prespecified structure by leveraging object manipulation actions. The primary technical challenge lies in the exponentially increasing complexity of identifying a feasible assembly sequence as the number of parts grows. To address this, we introduce a graph-based reinforcement learning approach, where a graph attention network is trained using a delayed reward assignment strategy. In this strategy, rewards are assigned only when an assembly action contributes to the successful completion of the assembly task. We validate the framework's performance through physics-based simulations, comparing it against various baselines to emphasize the significance of the proposed reward assignment approach. Additionally, we demonstrate the feasibility of deploying our framework in a real-world robotic assembly scenario.
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