用几何织物增强模仿学习，让机器人运动更安全稳定。
TamedPUMA: safe and stable imitation learning with geometric fabrics
- 将模仿学习与几何织物结合，统一建模为二阶动力系统。
- 实现碰撞避让和关节极限等物理约束的无缝融合。
- 在仿真与真实7自由度机械臂上验证了稳定性与安全性。
从动力系统角度出发，模仿学习（IL）为机器人提供了一种直观有效的任务空间运动教学方法，可实现目标收敛。然而，传统IL在保障安全性和满足物理约束方面存在严重局限。本文提出TamedPUMA，通过引入最新运动生成技术——几何织物（geometric fabrics），解决了该问题。由于IL策略与几何织物均将运动描述为人工二阶动力系统，我们设计两种变体，使IL提供几何织物的导航策略。结果是在保持稳定性的前提下，可自然融合如碰撞避免、关节极限等几何约束。除理论分析外，我们在仿真和真实世界任务中验证了TamedPUMA，包括7自由度机械臂。
原文摘要 · Abstract (English)
Using the language of dynamical systems, Imitation learning (IL) provides an intuitive and effective way of teaching stable task-space motions to robots with goal convergence. Yet, IL techniques are affected by serious limitations when it comes to ensuring safety and fulfillment of physical constraints. With this work, we solve this challenge via TamedPUMA, an IL algorithm augmented with a recent development in motion generation called geometric fabrics. As both the IL policy and geometric fabrics describe motions as artificial second-order dynamical systems, we propose two variations where IL provides a navigation policy for geometric fabrics. The result is a stable imitation learning strategy within which we can seamlessly blend geometrical constraints like collision avoidance and joint limits. Beyond providing a theoretical analysis, we demonstrate TamedPUMA with simulated and real-world tasks, including a 7-DoF manipulator.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。