no code implementations • 16 Jul 2024 • Navid Hashemi, Lars Lindemann, Jyotirmoy V. Deshmukh

Our method is based on three main steps: (1) learning a deterministic surrogate model from sampled trajectories, (2) conducting reachability analysis over the surrogate model, and (3) employing {\em robust conformal inference} using an additional set of sampled trajectories to quantify the surrogate model's distribution shift with respect to the deployed SCPS.

no code implementations • 17 May 2024 • Charis Stamouli, Lars Lindemann, George J. Pappas

We propose a shrinking-horizon MPC that guarantees recursive feasibility via a gradual relaxation of the safety constraints as new prediction regions become available online.

no code implementations • 5 May 2024 • Eleftherios E. Vlahakis, Lars Lindemann, Pantelis Sopasakis, Dimos V. Dimarogonas

We consider the control design of stochastic discrete-time linear multi-agent systems (MASs) under a global signal temporal logic (STL) specification to be satisfied at a predefined probability.

no code implementations • 27 Feb 2024 • Gregorio Marchesini, Siyuan Liu, Lars Lindemann, Dimos V. Dimarogonas

In this work, we propose a method to decompose signal temporal logic (STL) tasks for multi-agent systems subject to constraints imposed by the communication graph.

1 code implementation • 14 Feb 2024 • Yanfei Zhou, Lars Lindemann, Matteo Sesia

This paper presents a new conformal method for generating simultaneous forecasting bands guaranteed to cover the entire path of a new random trajectory with sufficiently high probability.

no code implementations • 12 Feb 2024 • Yiqi Zhao, Xinyi Yu, Jyotirmoy V. Deshmukh, Lars Lindemann

Motivated by the advances in conformal prediction (CP), we propose conformal predictive programming (CPP), an approach to solve chance constrained optimization (CCO) problems, i. e., optimization problems with nonlinear constraint functions affected by arbitrary random parameters.

no code implementations • 8 Jan 2024 • Farhad Mehdifar, Lars Lindemann, Charalampos P. Bechlioulis, Dimos V. Dimarogonas

This paper introduces a novel control framework to address the satisfaction of multiple time-varying output constraints in uncertain high-order MIMO nonlinear control systems.

1 code implementation • 12 Dec 2023 • Renukanandan Tumu, Matthew Cleaveland, Rahul Mangharam, George J. Pappas, Lars Lindemann

While prior work has gone into creating score functions that produce multi-model prediction regions, such regions are generally too complex for use in downstream planning and control problems.

1 code implementation • 7 Dec 2023 • Xinyi Yu, Yiqi Zhao, Xiang Yin, Lars Lindemann

We propose a predictive control synthesis framework that guarantees, with high probability, the satisfaction of signal temporal logic (STL) tasks that are defined over the system and uncontrollable stochastic agents.

1 code implementation • 27 Nov 2023 • Chuwei Wang, Xinyi Yu, Jianing Zhao, Lars Lindemann, Xiang Yin

Existing works on online monitoring usually assume that the monitor can acquire system information periodically at each time instant.

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 • 12 Nov 2023 • Eleftherios E. Vlahakis, Lars Lindemann, Dimos V. Dimarogonas

We consider the synthesis problem of a multi-agent system under signal temporal logic (STL) specifications representing bounded-time tasks that need to be satisfied recurrently over an infinite horizon.

no code implementations • 17 Sep 2023 • Navid Hashemi, Xin Qin, Lars Lindemann, Jyotirmoy V. Deshmukh

We consider data-driven reachability analysis of discrete-time stochastic dynamical systems using conformal inference.

no code implementations • 12 Aug 2023 • Xin Qin, Navid Hashemi, Lars Lindemann, Jyotirmoy V. Deshmukh

Ultimately, conformance can capture distance between design models and their real implementations and thus aid in robust system design.

no code implementations • 24 Jul 2023 • Xinyi Yu, Xiang Yin, Lars Lindemann

Given an ATR bound, we compute a sequence of control inputs so that the specification is satisfied by the system as long as each sub-trajectory is shifted not more than the ATR bound.

no code implementations • 12 Jul 2023 • Farhad Mehdifar, Lars Lindemann, Charalampos P. Bechlioulis, Dimos V. Dimarogonas

This paper proposes a novel control framework for handling (potentially coupled) multiple time-varying output constraints for uncertain nonlinear systems.

no code implementations • 8 Jun 2023 • Alëna Rodionova, Lars Lindemann, Manfred Morari, George J. Pappas

Many modern autonomous systems, particularly multi-agent systems, are time-critical and need to be robust against timing uncertainties.

1 code implementation • 3 Apr 2023 • Matthew Cleaveland, Insup Lee, George J. Pappas, Lars Lindemann

In fact, to obtain prediction regions over $T$ time steps with confidence $1-\delta$, {previous works require that each individual prediction region is valid} with confidence $1-\delta/T$.

no code implementations • 1 Apr 2023 • Shuo Yang, George J. Pappas, Rahul Mangharam, Lars Lindemann

However, these perception maps are not perfect and result in state estimation errors that can lead to unsafe system behavior.

no code implementations • 2 Feb 2023 • Renukanandan Tumu, Lars Lindemann, Truong Nghiem, Rahul Mangharam

Predicting the motion of dynamic agents is a critical task for guaranteeing the safety of autonomous systems.

no code implementations • 3 Nov 2022 • Lars Lindemann, Xin Qin, Jyotirmoy V. Deshmukh, George J. Pappas

The second algorithm constructs prediction regions for future system states first, and uses these to obtain a prediction region for the satisfaction measure.

no code implementations • 26 Aug 2022 • Matthew Cleaveland, Lars Lindemann, Radoslav Ivanov, George Pappas

Motivated by the fragility of neural network (NN) controllers in safety-critical applications, we present a data-driven framework for verifying the risk of stochastic dynamical systems with NN controllers.

1 code implementation • 7 Jun 2022 • Anton Xue, Lars Lindemann, Rajeev Alur

Neural networks are central to many emerging technologies, but verifying their correctness remains a major challenge.

no code implementations • 28 May 2022 • Lars Lindemann, Lejun Jiang, Nikolai Matni, George J. Pappas

For discrete-time stochastic processes, we show under which conditions the approximate STL robustness risk can even be computed exactly.

no code implementations • 8 Apr 2022 • Sleiman Safaoui, Lars Lindemann, Iman Shames, Tyler H. Summers

Our control approach relies on reformulating these risk predicates as deterministic predicates over mean and covariance states of the system.

1 code implementation • 2 Apr 2022 • Anton Xue, Lars Lindemann, Alexander Robey, Hamed Hassani, George J. Pappas, Rajeev Alur

Lipschitz constants of neural networks allow for guarantees of robustness in image classification, safety in controller design, and generalizability beyond the training data.

no code implementations • 29 Mar 2022 • Alëna Rodionova, Lars Lindemann, Manfred Morari, George J. Pappas

We study the temporal robustness of temporal logic specifications and show how to design temporally robust control laws for time-critical control systems.

1 code implementation • 5 Feb 2022 • Lars Lindemann, Alena Rodionova, George J. Pappas

We then define the temporal robustness risk by investigating the temporal robustness of the realizations of a stochastic signal.

1 code implementation • 18 Nov 2021 • Lars Lindemann, Alexander Robey, Lejun Jiang, Satyajeet Das, Stephen Tu, Nikolai Matni

Along with the optimization problem, we provide verifiable conditions in terms of the density of the data, smoothness of the system model and state estimator, and the size of the error bounds that guarantee validity of the obtained ROCBF.

no code implementations • 30 Aug 2021 • Lars Lindemann, George J. Pappas, Dimos V. Dimarogonas

Addressing these is pivotal to build fully autonomous systems and requires a systematic integration of planning and control.

1 code implementation • 6 Apr 2021 • Alena Rodionova, Lars Lindemann, Manfred Morari, George J. Pappas

We present a robust control framework for time-critical systems in which satisfying real-time constraints robustly is of utmost importance for the safety of the system.

no code implementations • 3 Apr 2021 • Lars Lindemann, Nikolai Matni, George J. Pappas

We then define the risk of a stochastic process not satisfying an STL formula robustly, referred to as the STL robustness risk.

no code implementations • 4 Feb 2021 • Lars Lindemann, Dimos V. Dimarogonas

Motivated by the recent interest in cyber-physical and autonomous robotic systems, we study the problem of dynamically coupled multi-agent systems under a set of signal temporal logic tasks.

1 code implementation • 16 Jan 2021 • Alexander Robey, Lars Lindemann, Stephen Tu, Nikolai Matni

We identify sufficient conditions on the data such that feasibility of the optimization problem ensures correctness of the learned robust hybrid control barrier functions.

no code implementations • 8 Nov 2020 • Lars Lindemann, Haimin Hu, Alexander Robey, Hanwen Zhang, Dimos V. Dimarogonas, Stephen Tu, Nikolai Matni

Motivated by the lack of systematic tools to obtain safe control laws for hybrid systems, we propose an optimization-based framework for learning certifiably safe control laws from data.

1 code implementation • 7 Apr 2020 • Alexander Robey, Haimin Hu, Lars Lindemann, Hanwen Zhang, Dimos V. Dimarogonas, Stephen Tu, Nikolai Matni

Furthermore, if the CBF parameterization is convex, then under mild assumptions, so is our learning process.

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