用可变刚度动作基元提升机器人连续接触任务的效率与适应性
Impedance Primitive-augmented Hierarchical Reinforcement Learning for Sequential Tasks
- 构建分层强化学习框架，通过可变刚度动作基元实现接触任务分步执行
- 在四类任务中成功提升学习效率与成功率，实测验证了从仿真到现实的迁移能力
- 适合需要柔顺控制的复杂交互任务，如抓取、开门等工业场景
本文提出一种基于阻抗基元的分层强化学习框架，用于高效完成机器人在连续接触任务中的操作。该框架利用分层结构，依次执行具备可变刚度控制能力的行为基元。核心包含三个组件：支持可变刚度的动作空间、执行过程中动态调整刚度的自适应控制器，以及促进探索并鼓励柔顺性的功能耦合机制。在方块搬运、开门、物体推动和表面清洁等训练环境中进行充分训练与评估，结果表明该方法显著提升了学习效率、基元选择的组合性及任务成功率，优于当前最优方法。真实世界测试进一步验证了其从仿真到现实的迁移能力。本工作为更灵活、多功能的机器人操作系统奠定了基础，有望应用于更复杂的接触型任务。
原文摘要 · Abstract (English)
This paper presents an Impedance Primitive-augmented hierarchical reinforcement learning framework for efficient robotic manipulation in sequential contact tasks. We leverage this hierarchical structure to sequentially execute behavior primitives with variable stiffness control capabilities for contact tasks. Our proposed approach relies on three key components: an action space enabling variable stiffness control, an adaptive stiffness controller for dynamic stiffness adjustments during primitive execution, and affordance coupling for efficient exploration while encouraging compliance. Through comprehensive training and evaluation, our framework learns efficient stiffness control capabilities and demonstrates improvements in learning efficiency, compositionality in primitive selection, and success rates compared to the state-of-the-art. The training environments include block lifting, door opening, object pushing, and surface cleaning. Real world evaluations further confirm the framework's sim2real capability. This work lays the foundation for more adaptive and versatile robotic manipulation systems, with potential applications in more complex contact-based tasks.
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