activity
20242026
collaborators

6 papers

cs.RO2026

Efficient Coordination and Synchronization of Multi-Robot Systems Under Recurring Linear Temporal Logic

Davide Peron, Victor Nan Fernandez-Ayala, Eleftherios E. Vlahakis +1

We consider multi-robot systems under recurring tasks formalized as linear temporal logic (LTL) specifications. To solve the planning problem efficiently, we propose a bottom-up ap…

eess.SY2026

Multi-Agent Temporal Logic Planning via Penalty Functions and Block-Coordinate Optimization

Eleftherios E. Vlahakis, Arash Bahari Kordabad, Lars Lindemann +3

Multi-agent planning under Signal Temporal Logic (STL) is often hindered by collaborative tasks that lead to computational challenges due to the inherent high dimensionality of the…

eess.SY2026

Conformal Prediction-Based MPC for Stochastic Linear Systems

Lukas Vogel, Andrea Carron, Eleftherios E. Vlahakis +1

We propose a stochastic model predictive control (MPC) framework for linear systems subject to joint-in-time chance constraints under unknown disturbance distributions. Unlike exis…

eess.SY2025

Conformal Data-driven Control of Stochastic Multi-Agent Systems under Collaborative Signal Temporal Logic Specifications

Eleftherios E. Vlahakis, Lars Lindemann, Dimos V. Dimarogonas

We address control synthesis of stochastic discrete-time linear multi-agent systems under jointly chance-constrained collaborative signal temporal logic specifications in a distrib…

eess.SY2025

Data-Driven Distributionally Robust Control for Interacting Agents under Logical Constraints

Arash Bahari Kordabad, Eleftherios E. Vlahakis, Lars Lindemann +3

In this paper, we propose a distributionally robust control synthesis for an agent with stochastic dynamics that interacts with other agents under uncertainties and constraints exp…

eess.SY2024

Conformal Prediction for Distribution-free Optimal Control of Linear Stochastic Systems

Eleftherios E. Vlahakis, Lars Lindemann, Pantelis Sopasakis +1

We address an optimal control problem for linear stochastic systems with unknown noise distributions and joint chance constraints using conformal prediction. Our approach involves…