no code implementations • 20 Dec 2023 • Chuanzheng Wang, Yiming Meng, Jun Liu, Stephen Smith
Control barrier functions are widely used to synthesize safety-critical controls.
no code implementations • 22 May 2022 • Chuanzheng Wang, Yiming Meng, Stephen L. Smith, Jun Liu
More specifically, we propose a data-driven stochastic control barrier function (DDSCBF) framework and use supervised learning to learn the unknown stochastic dynamics via the DDSCBF scheme.
no code implementations • 6 Apr 2021 • Chuanzheng Wang, Yiming Meng, Stephen L. Smith, Jun Liu
We propose a notion of stochastic control barrier functions (SCBFs)and show that SCBFs can significantly reduce the control efforts, especially in the presence of noise, compared to stochastic reciprocal control barrier functions (SRCBFs), and offer a less conservative estimation of safety probability, compared to stochastic zeroing control barrier functions (SZCBFs).
no code implementations • 2 Apr 2020 • Chuanzheng Wang, Yi-Nan Li, Stephen L. Smith, Jun Liu
A na\"ive way of solving a motion planning problem with LTL specifications using reinforcement learning is to sample a trajectory and then assign a high reward for training if the trajectory satisfies the entire LTL formula.