Publications

Conference Proceedings
Sampling-Based Control via Entropy-Regularized Optimal Transport
V. Pacelli, A. Ratheesh, E. A. Theodorou
Proceedings of Robotics: System and Science, 2026. Submitted; Under Review
Distributionally Robust Schrödinger Bridge
J. Sul, P. Theodoropoulos, V. Pacelli, J. Choi, E. A. Theodorou
Proceedings of the International Conference on Machine Learning, 2026. Submitted; Under Review
Operator Splitting Covariance Steering for Safe Stochastic Nonlinear Control
A. Ratheesh, V. Pacelli, A. D. Saravanos, E. A. Theodorou
Proceedings of the Conference on Decision and Control, pp. 3552–3559, 2025.
DOI arXiv Link
MetroSky: High-Fidelity Photorealistic Simulator for Urban Air Mobility Vehicles
A. Ratheesh, V. Pacelli, E. A. Theodorou
SCITECH Forum, 2025.
Link
Deep Distributed Optimization for Large-Scale Quadratic Programming
A. D. Saravanos, H. Kuperman, A. Oshin, A. T. Abdul, V. Pacelli, E. Theodorou
Proceedings of the International Conference on Learning Representations, 2025.
DOI arXiv Link
Feedback Schrödinger Bridge Matching
P. Theodoropoulos, N. Komianos, V. Pacelli, G.-H. Liu, E. A. Theodorou
Proceedings of the International Conference on Learning Representations, 2025. Oral
DOI arXiv Link
Fundamental Performance Limits for Sensor-Based Robot Control and Policy Learning
A. Majumdar, V. Pacelli
Proceedings of Robotics: System and Science, 2022.
DOI arXiv Link
Robust Control Under Uncertainty via Bounded Rationality and Differential Privacy
V. Pacelli, A. Majumdar
Proceedings of the International Conference on Robotics and Automation, pp. 3467--3474, 2022.
DOI arXiv Link
Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement Learning
A. Sonar, V. Pacelli, A. Majumdar
Proceedings of the Conference on Learning for Dynamics and Control, pp. 21--33, 2021.
arXiv Link
Learning Task-Driven Control Policies via Information Bottlenecks
V. Pacelli, A. Majumdar
Proceedings of Robotics: System and Science, 2020.
DOI arXiv Link
Task-Driven Estimation and Control via Information Bottlenecks
V. Pacelli, A. Majumdar
Proceedings of the International Conference on Robotics and Automation, pp. 2061--2067, 2019.
DOI Link
Integration of Local Geometry and Metric Information in Sampling-Based Motion Planning
V. Pacelli, O. Arslan, D. E. Koditschek
Proceedings of the International Conference on Robotics and Automation, pp. 3061--3068, 2018.
DOI Link
Sensory Steering for Sampling-Based Motion Planning
O. Arslan, V. Pacelli, D. E. Koditschek
Proceedings of the International Conference on Intelligent Robots and Systems, pp. 3708--3715, 2017.
DOI Link
Journal Articles
Fundamental Limits for Sensor-Based Control via the Gibbs Variational Principle
V. Pacelli, E. A. Theodorou
Control Systems Letters, 2026. Submitted; Under Review
arXiv
Fundamental Limits for Sensor-Based Robot Control
A. Majumdar, Z. Mei, V. Pacelli
International Journal of Robotics Research, vol. 42, no. 12, pp. 1051--1069, 2023.
DOI arXiv
Patent
Systems of Stacking Interlocking Blocks
R. Mangharam, M. E. O'Kelly, V. Pacelli, M. A. Brady
U.S. Patent 11,213,747, 2022.
Patent
Dissertations
Information-Theoretic Necessary and Sufficient Conditions for the Task-Driven Control of Robots
V. Pacelli
Ph.D. Dissertation, Princeton University, 2023, 2023.
Joint Exploration of Local Metrics and Geometry in Sampling-Based Planning
V. Pacelli
M.S. Thesis, University of Pennsylvania, 2017, 2017.