Neural Integral Control Barrier Functions for Agile Collision Avoidance
Overview
This paper introduces a learning-based approach to integral control barrier functions (ICBFs) that jointly learns dynamics, nominal controllers, and barrier functions from data. By training the barrier on viability rather than geometry and incorporating actuator limits as hard bounds, the method ensures safety even under model errors. Experiments on a 6-D dynamic bicycle demonstrate that the proposed filter removes all 12 unsafe episodes from 200 held-out layouts while operating above 100 Hz on a single CPU core, outperforming a jointly trained baseline in constraint adherence.
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- arXiv Robotics — research abstractsSignalNeural Integral Control Barrier Functions for Agile Collision Avoidance
This paper introduces a learning-based approach to integral control barrier functions (ICBFs) that jointly learns dynamics, nominal controllers, and barrier functions from data. By training the barrier on viability rather than geometry and incorporating actuator limits as hard bounds, the method ensures safety even under model errors. Experiments on a 6-D dynamic bicycle demonstrate that the proposed filter removes all 12 unsafe episodes from 200 held-out layouts while operating above 100 Hz on a single CPU core, outperforming a jointly trained baseline in constraint adherence.
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