融合传统控制与学习，实现机器人实时安全避障
GUARD: Toward a Compromise between Traditional Control and Learning for Safe Robot Systems
- 结合反应式模型预测轮廓控制与不确定性溯源的主动学习
- 实时利用概率核优化处理感知不确定性，提升避障可靠性
- 适合需高安全性与灵活性的自主机器人系统应用
本文提出框架GUARD（基于不确定性归因与概率核优化的引导式机器人控制），将传统控制与感知不确定性意识的学习方法结合，实现实时安全的机器人碰撞避免。通过将反应式模型预测轮廓控制（RMPCC）与迭代最近点（ICP）算法融合，并引入基于主动学习的在线不确定性源归因机制，配合概率核优化技术，有效解决机器人领域中“安全”定义模糊的核心挑战。实验表明，GUARD具有优异性能，凸显其未来拓展应用的重要价值。
原文摘要 · Abstract (English)
This paper presents the framework \textbf{GUARD} (\textbf{G}uided robot control via \textbf{U}ncertainty attribution and prob\textbf{A}bilistic kernel optimization for \textbf{R}isk-aware \textbf{D}ecision making) that combines traditional control with an uncertainty-aware perception technique using active learning with real-time capability for safe robot collision avoidance. By doing so, this manuscript addresses the central challenge in robotics of finding a reasonable compromise between traditional methods and learning algorithms to foster the development of safe, yet efficient and flexible applications. By unifying a reactive model predictive countouring control (RMPCC) with an Iterative Closest Point (ICP) algorithm that enables the attribution of uncertainty sources online using active learning with real-time capability via a probabilistic kernel optimization technique, \emph{GUARD} inherently handles the existing ambiguity of the term \textit{safety} that exists in robotics literature. Experimental studies indicate the high performance of \emph{GUARD}, thereby highlighting the relevance and need to broaden its applicability in future.
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