text: 154,172,1524078294458462209,1278822973575577601,Sander Tonkens,"🚨New exciting work: <LINK> with @SylviaLHerbert  We propose a simple yet powerful method to refine approximately valid CBFs (e.g. neural CBFs) using dynamic programming based reachability analysis, to guarantee safety. (1/n) 🧵👇 Our method iteratively updates a CBF (pink to green).  We prove that our method becomes provably less unsafe with every iteration, and converges to a valid CBF. In practice, we often recover the largest safe set (shaded green) (2/n) <LINK> We show that we can update conservative CBFs, rendering the safety filter less invasive, while remaining safe!  We are particularly excited about leveraging this method on hardware (currently ongoing!) to adapt to sudden changes in the environment (3/n) <LINK> In addition, we can refine invalid CBFs to guarantee safety. We provide an open-source implementation <LINK>. Give it a try! We also provide an intuitive CBF library in python: <LINK> (4/n) <LINK>",http://arxiv.org/abs/2204.12507,"Safety filters based on Control Barrier Functions (CBFs) have emerged as a practical tool for the safety-critical control of autonomous systems. These approaches encode safety through a value function and enforce safety by imposing a constraint on the time derivative of this value function. However, synthesizing a valid CBF that is not overly conservative in the presence of input constraints is a notorious challenge. In this work, we propose refining candidate CBFs using formal verification methods to obtain a valid CBF. In particular, we update an expert-synthesized or backup CBF using dynamic programming (DP) based reachability analysis. Our framework guarantees that with every DP iteration the obtained CBF is provably at least as safe as the prior iteration and converges to a valid CBF. Therefore, our proposed method can be used in-the-loop for robotic systems. We demonstrate the practicality of our method to enhance safety and/or reduce conservativeness on a range of nonlinear control-affine systems using various CBF synthesis techniques in simulation. ",Refining Control Barrier Functions through Hamilton-Jacobi Reachability,4,"['🚨New exciting work: <LINK> with @SylviaLHerbert \n\nWe propose a simple yet powerful method to refine approximately valid CBFs (e.g. neural CBFs) using dynamic programming based reachability analysis, to guarantee safety. \n(1/n) 🧵👇', 'Our method iteratively updates a CBF (pink to green). \n\nWe prove that our method becomes provably less unsafe with every iteration, and converges to a valid CBF. In practice, we often recover the largest safe set (shaded green) (2/n) https://t.co/f0nYvBFSfS', 'We show that we can update conservative CBFs, rendering the safety filter less invasive, while remaining safe! \n\nWe are particularly excited about leveraging this method on hardware (currently ongoing!) to adapt to sudden changes in the environment (3/n) https://t.co/cMmRAOq3ya', 'In addition, we can refine invalid CBFs to guarantee safety. We provide an open-source implementation https://t.co/nwaVQetGzO. Give it a try!\n\nWe also provide an intuitive CBF library in python: https://t.co/omJ5MuKtI5 \n(4/n) https://t.co/7xzaf8oL5n']",22,04,925