63 citations · 81 across the 42 of their papers we have counts for
21 papers · 1 filter
Controller-Agnostic Runtime Safety Certificates for Tool-Using AI Agents via Data-Driven Robust Reachability
Ali Baheri, Peng Wei
Software agents built on large language models act by issuing tool calls, some of which cannot be undone. To control theory the model is an opaque feedback controller, replaced eve…
Flow-Corrected Thompson Sampling for Non-Stationary Contextual Bandits
Ali Baheri, AmirHossein Naghdi
We study non-stationary linear contextual bandits where the reward model drifts over time, rendering classical contextual bandit algorithms brittle because historical data becomes…
Wasserstein Stability of Contracting Flows: Effective Rates, Euler Self-Correction, and Noise Tightening
Ali Baheri
Contraction theory guaranties exponential convergence between trajectories of a stable nonlinear system. When initial conditions are uncertain and represented as probability distri…
Geometry-Aware Decentralized Sinkhorn for Wasserstein Barycenters
Ali Baheri, Alireza Vahid
Distributed systems require fusing heterogeneous local probability distributions into a global summary over sparse and unreliable communication networks. Traditional consensus algo…
Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning
Ali Baheri
Cooperative multi-agent reinforcement learning (MARL) demands principled mechanisms to align heterogeneous policies while preserving the capacity for specialized behavior. We intro…
Implicit Constraint-Aware Off-Policy Correction for Offline Reinforcement Learning
Ali Baheri
Offline reinforcement learning promises policy improvement from logged interaction data alone, yet state-of-the-art algorithms remain vulnerable to value over-estimation and to vio…
SMTL: A Stratified Logic for Expressive Multi-Level Temporal Specifications
Ali Baheri, Peng Wei
We present Stratified Metric Temporal Logic (SMTL), a novel formalism for specifying and verifying properties of complex cyber-physical systems that exhibit behaviors across multip…
Optimizing Falsification for Learning-Based Control Systems: A Multi-Fidelity Bayesian Approach
Zahra Shahrooei, Mykel J. Kochenderfer, Ali Baheri
Testing controllers in safety-critical systems is vital for ensuring their safety and preventing failures. In this paper, we address the falsification problem within learning-based…
Concurrent Learning of Policy and Unknown Safety Constraints in Reinforcement Learning
Lunet Yifru, Ali Baheri
Reinforcement learning (RL) has revolutionized decision-making across a wide range of domains over the past few decades. Yet, deploying RL policies in real-world scenarios presents…
BEACON: A Bayesian Evolutionary Approach for Counterexample Generation of Control Systems
Joshua Yancosek, Ali Baheri
The rigorous safety verification of control systems in critical applications is essential, given their increasing complexity and integration into everyday life. Simulation-based fa…
Joint Falsification and Fidelity Settings Optimization for Validation of Safety-Critical Systems: A Theoretical Analysis
Ali Baheri, Mykel J. Kochenderfer
Safety validation is a crucial component in the development and deployment of autonomous systems, such as self-driving vehicles and robotic systems. Ensuring safe operation necessi…
Joint Learning of Policy with Unknown Temporal Constraints for Safe Reinforcement Learning
Lunet Yifru, Ali Baheri
In many real-world applications, safety constraints for reinforcement learning (RL) algorithms are either unknown or not explicitly defined. We propose a framework that concurrentl…
Falsification of Learning-Based Controllers through Multi-Fidelity Bayesian Optimization
Zahra Shahrooei, Mykel J. Kochenderfer, Ali Baheri
Simulation-based falsification is a practical testing method to increase confidence that the system will meet safety requirements. Because full-fidelity simulations can be computat…
A Survey on Reinforcement Learning in Aviation Applications
Pouria Razzaghi, Amin Tabrizian, Wei Guo +6
Compared with model-based control and optimization methods, reinforcement learning (RL) provides a data-driven, learning-based framework to formulate and solve sequential decision-…
A Verification Framework for Certifying Learning-Based Safety-Critical Aviation Systems
Ali Baheri, Hao Ren, Benjamin Johnson +2
We present a safety verification framework for design-time and run-time assurance of learning-based components in aviation systems. Our proposed framework integrates two novel meth…
Black-Box Safety Validation of Autonomous Systems: A Multi-Fidelity Reinforcement Learning Approach
Jared J. Beard, Ali Baheri
The increasing use of autonomous and semi-autonomous agents in society has made it crucial to validate their safety. However, the complex scenarios in which they are used may make…
Safe Reinforcement Learning with Mixture Density Network: A Case Study in Autonomous Highway Driving
Ali Baheri
This paper presents a safe reinforcement learning system for automated driving that benefits from multimodal future trajectory predictions. We propose a safety system that consists…
Vision-Based Autonomous Driving: A Model Learning Approach
Ali Baheri, Ilya Kolmanovsky, Anouck Girard +2
We present an integrated approach for perception and control for an autonomous vehicle and demonstrate this approach in a high-fidelity urban driving simulator. Our approach first…
Deep Reinforcement Learning with Enhanced Safety for Autonomous Highway Driving
Ali Baheri, Subramanya Nageshrao, H. Eric Tseng +3
In this paper, we present a safe deep reinforcement learning system for automated driving. The proposed framework leverages merits of both rule-based and learning-based approaches…
Waypoint Optimization Using Bayesian Optimization: A Case Study in Airborne Wind Energy Systems
Ali Baheri, Chris Vermillion
We present a data-driven optimization framework that aims to address online adaptation of the flight path shape for an airborne wind energy system (AWE) that follows a repetitive p…
Economically Efficient Combined Plant and Controller Design Using Batch Bayesian Optimization: Mathematical Framework and Airborne Wind Energy Case Study
Ali Baheri, Chris Vermillion
We present a novel data-driven nested optimization framework that addresses the problem of coupling between plant and controller optimization. This optimization strategy is tailore…