collaborators

5 papers

eess.SY2026

Learning Input-Constrained Funnel Controllers from State Trajectory Data

Panagiotis S. Trakas, Omid Mirzaeedodangeh, Lars Lindemann

Designing feedback controllers that satisfy predefined performance specifications while enforcing hard input constraints is a challenging task. Our work is motivated by the idea th…

cs.RO2026

Latent Activation Editing: Inference-Time Refinement of Learned Policies for Safer Multirobot Navigation

Satyajeet Das, Darren Chiu, Zhehui Huang +2

Reinforcement learning has enabled significant progress in complex domains such as coordinating and navigating multiple quadrotors. However, even well-trained policies remain vulne…

eess.SY2026

Safe Planning in Interactive Environments via Iterative Policy Updates and Adversarially Robust Conformal Prediction

Omid Mirzaeedodangeh, Eliot Shekhtman, Nikolai Matni +1

Safe planning of an autonomous agent in interactive environments -- such as the control of a self-driving vehicle among pedestrians -- poses a major challenge as the behavior of th…

cs.RO2026

V-MORALS: Visual Morse Graph-Aided Estimation of Regions of Attraction in a Learned Latent Space

Faiz Aladin, Ashwin Balasubramanian, Lars Lindemann +1

Reachability analysis has become increasingly important in robotics to distinguish safe from unsafe states. Unfortunately, existing reachability and safety analysis methods often f…

cs.RO2025

Sample-Efficient Expert Query Control in Active Imitation Learning via Conformal Prediction

Arad Firouzkouhi, Omid Mirzaeedodangeh, Lars Lindemann

Active imitation learning (AIL) combats covariate shift by querying an expert during training. However, expert action labeling often dominates the cost, especially in GPU-intensive…