Estimating Control Barriers from Offline Data

Hongzhan Yu, Seth Farrell, Ryo Yoshimitsu, Zhizhen Qin, Henrik I Christensen, Sicun Gao

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.