no code implementations • 23 Mar 2024 • Navid Hashemi, Bardh Hoxha, Danil Prokhorov, Georgios Fainekos, Jyotirmoy Deshmukh
We show how this learning problem is similar to training recurrent neural networks (RNNs), where the number of recurrent units is proportional to the temporal horizon of the agent's task objectives.
1 code implementation • 16 Nov 2023 • Yiqi Zhao, Bardh Hoxha, Georgios Fainekos, Jyotirmoy V. Deshmukh, Lars Lindemann
To address these challenges, we assume to know an upper bound on the statistical distance (in terms of an f-divergence) between the distributions at deployment and design time, and we utilize techniques based on robust conformal prediction.
no code implementations • 3 Apr 2023 • Mitchell Black, Georgios Fainekos, Bardh Hoxha, Danil Prokhorov, Dimitra Panagou
We propose a novel class of risk-aware control barrier functions (RA-CBFs) for the control of stochastic safety-critical systems.
no code implementations • 7 Mar 2023 • Navid Hashemi, Bardh Hoxha, Tomoya Yamaguchi, Danil Prokhorov, Geogios Fainekos, Jyotirmoy Deshmukh
In this paper, we present a model for the verification of Neural Network (NN) controllers for general STL specifications using a custom neural architecture where we map an STL formula into a feed-forward neural network with ReLU activation.
no code implementations • 14 Oct 2022 • Navid Hashemi, Xin Qin, Jyotirmoy V. Deshmukh, Georgios Fainekos, Bardh Hoxha, Danil Prokhorov, Tomoya Yamaguchi
In this paper, we consider the problem of synthesizing a controller in the presence of uncertainty such that the resulting closed-loop system satisfies certain hard constraints while optimizing certain (soft) performance objectives.
1 code implementation • 29 Jun 2022 • Mohammad Hekmatnejad, Bardh Hoxha, Jyotirmoy V. Deshmukh, Yezhou Yang, Georgios Fainekos
Automated vehicles (AV) heavily depend on robust perception systems.
no code implementations • 30 Dec 2021 • Shakiba Yaghoubi, Georgios Fainekos, Tomoya Yamaguchi, Danil Prokhorov, Bardh Hoxha
Our goal is to design controllers that bound the probability of a system failure in finite-time to a given desired value.
no code implementations • 9 Aug 2021 • Xiaodong Yang, Tom Yamaguchi, Hoang-Dung Tran, Bardh Hoxha, Taylor T Johnson, Danil Prokhorov
Formally verifying the safety and robustness of well-trained DNNs and learning-enabled systems under attacks, model uncertainties, and sensing errors is essential for safe autonomy.
no code implementations • 22 Jun 2021 • Xiaodong Yang, Tomoya Yamaguchi, Hoang-Dung Tran, Bardh Hoxha, Taylor T Johnson, Danil Prokhorov
Besides the computation of reachable sets, our approach is also capable of backtracking to the input domain given an output reachable set.
no code implementations • 25 Apr 2020 • Mohammad Hekmatnejad, Bardh Hoxha, Georgios Fainekos
The safety of Automated Vehicles (AV) as Cyber-Physical Systems (CPS) depends on the safety of their consisting modules (software and hardware) and their rigorous integration.