用行为树整合动态运动基元，让机器人从演示中学习无需预设动作的可解释技能。
Beyond Predefined Actions: Integrating Behavior Trees and Dynamic Movement Primitives for Robot Learning from Demonstration
- 将动态运动基元嵌入行为树框架，联合学习结构与动作
- 仅需一次示范即可生成可修改的完整任务策略
- 兼具可解释性、模块化与适应性，适合复杂任务学习
可解释的策略表示如行为树（BTs）和动态运动基元（DMPs）能实现机器人技能从人类示范中的迁移，但各有局限：行为树需专家定义底层动作，而动态运动基元缺乏高层任务逻辑。本文通过将DMP控制器集成到行为树框架中，从单一示范中联合学习行为树结构与动态运动基元动作，消除了对预定义动作的需求。同时，结合行为树决策逻辑与动态运动基元运动生成，方法提升了策略的可解释性、模块化和适应性。该方法不仅能学习复现低层运动，还能将部分示范组合成连贯且易于修改的整体策略。
原文摘要 · Abstract (English)
Interpretable policy representations like Behavior Trees (BTs) and Dynamic Motion Primitives (DMPs) enable robot skill transfer from human demonstrations, but each faces limitations: BTs require expert-crafted low-level actions, while DMPs lack high-level task logic. We address these limitations by integrating DMP controllers into a BT framework, jointly learning the BT structure and DMP actions from single demonstrations, thereby removing the need for predefined actions. Additionally, by combining BT decision logic with DMP motion generation, our method enhances policy interpretability, modularity, and adaptability for autonomous systems. Our approach readily affords both learning to replicate low-level motions and combining partial demonstrations into a coherent and easy-to-modify overall policy.
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