SafeFlowMPC: 基于学习策略的机器人机械臂预测与安全轨迹规划
SafeFlowMPC: Predictive and Safe Trajectory Planning for Robot Manipulators with Learning-based Policies
AI总结：
SafeFlowMPC结合流匹配和在线优化，为机器人机械臂提供安全且灵活的预测轨迹规划方法，通过实验验证其在抓取和人机交互任务中的高效性能。
摘要：
机器人逐步融入日常生活带来了诸多重大挑战。与传统工业应用相比，需要在实时反应中具有更高的灵活性。基于学习的方法可以通过演示轨迹训练出强大的策略，使机器人能够将任务泛化到相似情境。然而，这些黑箱模型缺乏可解释性和严格的安全保证。基于优化的方法提供这些保证，但缺乏所需的灵活性和泛化能力。本文提出SafeFlowMPC，结合流匹配和在线优化，融合学习和优化的优势。该方法在所有情况下都保证安全，并通过使用次优模型预测控制公式来满足实时执行的需求。SafeFlowMPC在KUKA 7-自由度机械臂上进行了三个实际实验，包括两个抓取实验和一个动态人机物体交接实验，取得了优异性能。实验视频可在http://www.acin.tuwien.ac.at/42d6获取，代码可在https://github.com/TU-Wien-ACIN-CDS/SafeFlowMPC获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
