オフラインデータからの制御バリア関数の推定
Estimating Control Barriers from Offline Data
訓練前に収集した固定のラベル付きデータセットからニューラル制御バリア関数を学習する枠組みを提案し、実機での動的障害物回避において安全性と保守性の改善を実現した。
著者: Hongzhan Yu, Seth Farrell, Ryo Yoshimitsu, Zhizhen Qin, Henrik I. Christensen, Sicun Gao
分類: eess.SY, cs.AI, cs.RO, cs.SY
原文アブストラクト
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.
