Estimating Control Barriers from Offline Data

• URL: http://arxiv.org/abs/2503.10641v1
• Date: Fri, 21 Feb 2025 04:55:20 GMT
• Title: Estimating Control Barriers from Offline Data
• Authors: Hongzhan Yu, Seth Farrell, Ryo Yoshimitsu, Zhizhen Qin, Henrik I. Christensen, Sicun Gao,
• Abstract summary: We propose a novel framework for learning neural CBFs through a fixed, sparsely-labeled dataset collected prior to training.<n>With limited amount of offline data, it achieves state-of-the-art performance for dynamic obstacle avoidance.
• Score: 14.241303913878887
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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