用少量离线数据训练安全机器人控制，无需在线采样或专家控制器。
Estimating Control Barriers from Offline Data
- 基于分布外分析的标注技术，从稀疏标签数据中高效传播知识。
- 仅用有限离线数据即实现最优动态避障性能，比现有方法更安全且保守程度更低。
- 适合无专家控制、数据稀缺的真实机器人场景，尤其适用于安全关键任务。
基于学习的方法在构建控制屏障函数（CBFs）以保障机器人安全控制方面日益流行。现有方法的主要局限在于依赖对状态空间的大规模采样或仿真中的在线系统交互。本文提出一种新框架，通过预先收集的固定、稀疏标注数据集来学习神经控制屏障函数。该方法引入基于分布外分析的新标注技术，实现从有限标注数据到未标注数据的知识高效传播。同时，摆脱对高性能专家控制器的依赖，允许在数据收集期间使用多个次优策略甚至人工控制。我们在真实平台进行了评估，仅使用少量离线数据，即在动态障碍物避让任务中达到当前最优性能，相较于现有方法表现出统计上更安全且更少保守的操控行为。
原文摘要 · Abstract (English)
Learning-based methods for constructing control barrier functions (CBFs) are gaining popularity for ensuring safe robot control. A major limitation of existing methods is their reliance on extensive sampling over the state space or online system interaction in simulation. In this work we propose a novel framework for learning neural CBFs through a fixed, sparsely-labeled dataset collected prior to training. Our approach introduces new annotation techniques based on out-of-distribution analysis, enabling efficient knowledge propagation from the limited labeled data to the unlabeled data. We also eliminate the dependency on a high-performance expert controller, and allow multiple sub-optimal policies or even manual control during data collection. We evaluate the proposed method on real-world platforms. With limited amount of offline data, it achieves state-of-the-art performance for dynamic obstacle avoidance, demonstrating statistically safer and less conservative maneuvers compared to existing methods.
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