用学习+优化融合方法，让机械臂安全实时抓物
SafeFlowMPC: Predictive and Safe Trajectory Planning for Robot Manipulators with Learning-based Policies
- 结合流匹配与在线优化，兼顾学习泛化与安全约束
- 在KUKA七自由度机械臂上实现3项真实任务，全程安全
- 适合需要实时性与安全性的智能机器人应用
机器人融入日常生活带来新挑战：相比传统工业应用，需更强灵活性与实时响应。基于学习的方法可从示范轨迹中训练强大策略，使机器人泛化到相似场景，但其黑箱特性缺乏可解释性与严格安全保证。优化方法虽能提供安全保证，却难以满足灵活性与泛化需求。本文提出SafeFlowMPC，融合流匹配与在线模型预测控制，结合学习与优化优势。该方法在任意时刻均保证安全，并通过次优的模型预测控制形式满足实时执行要求。在KUKA七自由度机械臂上完成三项真实实验，包括两项抓取任务和一项动态人机物品交接任务，表现优异。实验视频见http://www.acin.tuwien.ac.at/42d6，代码开源于https://github.com/TU-Wien-ACIN-CDS/SafeFlowMPC。
原文摘要 · Abstract (English)
The emerging integration of robots into everyday life brings several major challenges. Compared to classical industrial applications, more flexibility is needed in combination with real-time reactivity. Learning-based methods can train powerful policies based on demonstrated trajectories, such that the robot generalizes a task to similar situations. However, these black-box models lack interpretability and rigorous safety guarantees. Optimization-based methods provide these guarantees but lack the required flexibility and generalization capabilities. This work proposes SafeFlowMPC, a combination of flow matching and online optimization to combine the strengths of learning and optimization. This method guarantees safety at all times and is designed to meet the demands of real-time execution by using a suboptimal model-predictive control formulation. SafeFlowMPC achieves strong performance in three real-world experiments on a KUKA 7-DoF manipulator, namely two grasping experiment and a dynamic human-robot object handover experiment. A video of the experiments is available at http://www.acin.tuwien.ac.at/42d6. The code is available at https://github.com/TU-Wien-ACIN-CDS/SafeFlowMPC.
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