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Neural Control Barrier Functions for Safe Navigation
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Autonomous robot navigation can be particularly demanding, especially when the surrounding environment is not known and safety of the robot is crucial. This work relates to the synthesis of Control Barrier Functions (CBFs) through data for safe navigation in unknown environments. A novel methodology to jointly learn CBFs and corresponding safe controllers, in simulation, inspired by the State Dependent Riccati Equation (SDRE) is proposed. The CBF is used to obtain admissible commands from any nominal, possibly unsafe controller. An approach to apply the CBF inside a safety filter without the need for a consistent map or position estimate is developed. Subsequently, the resulting reactive safety filter is deployed on a multirotor platform integrating a LiDAR sensor both in simulation and real-world experiments.
Forward citations
Cited by 2 Pith papers
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Discriminative Barrier Functions for Safe Adversarial Imitation Learning from Observation
Constraining the adversarial imitation learning discriminator to discrete-time control barrier functions recovers safety barriers from unlabeled observations and reduces collisions in navigation.
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Safety Certification in the Latent space using Control Barrier Functions and World Models
A semi-supervised framework learns a control barrier certificate in the latent space of a DINO-v2-based world model for safe visuomotor control.
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