Publications

2023

  1. Q. H. Ho, Z. N. Sunberg, and M. Lahijanian, “Planning with SiMBA: Motion Planning under Uncertainty for Temporal Goals using Simplified Belief Guides,” in IEEE Conference on Robotics and Automation (ICRA), London, England, UK, 2023. (to appear)

  2. R. B. Ilyes, Q. H. Ho, and M. Lahijanian, “Stochastic Robustness Interval for Motion Planning with Signal Temporal Logic,” in IEEE Conference on Robotics and Automation (ICRA), London, England, UK, 2023. (to appear)

  3. A. Theurkauf, Q. H. Ho, R. Ilyes, N. Ahmed, and M. Lahijanian, “Chance-Constrained Motion Planning with Event-Triggered Estimation,” in IEEE Conference on Robotics and Automation (ICRA), London, England, UK, 2023. (to appear)

  4. G. Delimpaltadakis, M. Lahijanian, M. Mazo Jr., and L. Laurenti, “Interval Markov Decision Processes with Continuous Action-Spaces,” in International Conference on Hybrid Systems: Computation and Control (HSCC), San Antonio, TX, USA, 2023. (to appear)

2022

  1. Q. H. Ho, R. B. Ilyes, Z. Sunberg, and M. Lahijanian, “Automaton-Guided Control Synthesis for Signal Temporal Logic Specifications,” in IEEE Conference on Decision and Control (CDC), Cancun, Mexico, 2022. (to appear)

  2. A. Theurkauf, N. Ahmed, and M. Lahijanian, “Pareto Optimal Strategies for Event Triggered Estimation,” in IEEE Conference on Decision and Control (CDC), Cancun, Mexico, 2022. (to appear)

  3. G. O. Berger, M. Narasimhamurthy, K. Watanabe, M. Lahijanian, and S. Sankaranarayanan, “An Algorithm for Learning Switched Linear Dynamics from Data,” in Advances in Neural Information Processing Systems (NeurIPS), New Orleans, Louisiana, USA, 2022.

  4. R. Mazouz, K. Muvvala, A. Ratheesh Babu, L. Laurenti, and M. Lahijanian, “Safety Guarantees for Neural Network Dynamic Systems via Stochastic Barrier Functions,” in Advances in Neural Information Processing Systems (NeurIPS), New Orleans, Louisiana, USA, 2022.

  5. J. Kottinger, S. Almagor, and M. Lahijanian, “Conflict-based Search for Multi-Robot Motion Planning with Kinodynamic Constraints,” in Int’l Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 2022. (to appear)

  6. I. Nazmy, A. Harris, M. Lahijanian, and H. Schaub, “Shielded Deep Reinforcement Learning for Multi-Sensor Spacecraft Imaging,” in American Control Conference (ACC), Atlanta, GA, USA, 2022.

  7. J. Kottinger, S. Almagor, and M. Lahijanian, “Conflict-Based Search for Explainable Multi-Agent Path Finding,” in Int’l Conference on Automated Planning and Scheduling (ICAPS), Singapore, 2022.

  8. Q. H. Ho, Z. Sunberg, and M. Lahijanian, “Gaussian Belief Trees for Chance Constrained Asymptotically Optimal Motion Planning,” in IEEE Conference on Robotics and Automation (ICRA), 2022.

  9. K. Muvvala, P. Amorese, and M. Lahijanian, “Let’s Collaborate: Regret-based Reactive Synthesis for Robotic Manipulation,” in IEEE Conference on Robotics and Automation (ICRA), 2022.

  10. J. McMahon et al., “Expert-Informed Autonomous Science Planning for In-situ Observations and Discoveries,” in IEEE Aerospace Conference, 2022.

  11. S. A. Adams, M. Lahijanian, and L. Laurenti, “Formal Control Synthesis for Stochastic Neural Network Dynamic Models,” IEEE Control Systems Letters (L-CSS), 2022.

2021

  1. J. Jackson, L. Laurenti, E. Frew, and M. Lahijanian, “Synergistic Offline-Online Control Synthesis via Local Gaussian Process Regression,” in IEEE Conference on Decision and Control (CDC), 2021.

  2. K. Watanabe, N. Renninger, S. Sankaranarayanan, and M. Lahijanian, “Task Learning with Preferences for Planning with Safety Constraints,” in Workshop on Accessibility of Robot Programming and the Work of the Future, 2021.

  3. K. Watanabe, N. Renninger, S. Sankaranarayanan, and M. Lahijanian, “Probabilistic Specification Learning for Planning with Safety Constraints,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021.

  4. J. Jackson, L. Laurenti, E. Frew, and M. Lahijanian, “Towards Safe, Abstraction-based Online Learning and Synthesis for Unknown Systems,” in Workshop on Integrating Planning and Learning, 2021.

  5. K. Muvvala and M. Lahijanian, “Reactive Synthesis for Human-aware Robotic Manipulation using Regret Games,” in RSS Workshop on Robotics for People: Perspectives on Interaction, Learning and Safety, 2021.

  6. J. Kottinger, S. Almagor, and M. Lahijanian, “MAPS-X: Explainable Multi-Robot Motion Planning via Segmentation,” in Int’l Conference on Robotics and Automation (ICRA), Xi’an, China, 2021.

  7. A. M. Wells, Z. Kingston, M. Lahijanian, L. E. Kavraki, and M. Y. Vardi, “Finite Horizon Synthesis for Probabilistic Manipulation Domains,” in International Conference on Robotics and Automation (ICRA), Xi’an, China, 2021.

  8. J. Jackson, L. Laurenti, E. Frew, and M. Lahijanian, “Strategy synthesis for partially-known switched stochastic systems,” in Conference on Hybrid Systems: Computation and Control (HSCC), 2021, pp. 1–11.

  9. È. Pairet, J. D. Hernández, M. Carreras, Y. Petillot, and M. Lahijanian, “Online Mapping and Motion Planning under Uncertainty for Probabilistically Safe Autonomous Navigation,” IEEE Transactions on Automation Science and Engineering, 2021.

2020

  1. J. Jackson, L. Laurenti, E. Frew, and M. Lahijanian, “Towards Data-driven Verification of Unknown Dynamical Systems,” in Workshop on Robust Autonomy: Tools for Safety in Real-World Uncertain Environments, 2020.

  2. J. Kottinger, S. Shaull Almagor, and M. Lahijanian, “Explainable Multi-Agent Path Planning,” in Workshop on Explainable and Trustworthy Robot Decision Making for Scientific Data Collection, 2020.

  3. L. Laurenti, M. Lahijanian, A. Abate, L. Cardelli, and M. Kwiatkowska, “Formal and efficient synthesis for continuous-time linear stochastic hybrid processes,” IEEE Transactions on Automatic Control, vol. 66, no. 1, pp. 17–32, 2020.

  4. S. Almagor and M. Lahijanian, “Explainable Multi Agent Path Finding,” in Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, Richland, SC, 2020, pp. 34–42.

  5. J. Jackson, L. Laurenti, E. Frew, and M. Lahijanian, “Safety verification of unknown dynamical systems via gaussian process regression,” in 2020 IEEE 59th Conference on Decision and Control (CDC), 2020.

  6. A. M. Wells, M. Lahijanian, L. E. Kavraki, and M. Y. Vardi, “LTLf Synthesis on Probabilistic Systems,” in International Symposium on Games, Automata, Logics, and Formal Verification (GandALF), 2020.

2019

  1. F. Eiras and M. Lahijanian, “Towards Provably Correct Driver Assistance Systems through Stochastic Cognitive Modeling,” in Robotics: Science and Systems Workshop on Robust Autonomy: Tools for Safety in Real-World Uncertain Environments, 2019.

  2. N. Cauchi, L. Laurenti, M. Lahijanian, A. Abate, M. Kwiatkowska, and L. Cardelli, “Efficiency through Uncertainty: Scalable Formal Synthesis for Stochastic Hybrid Systems,” in Proceedings of the 2019 22nd ACM International Conference on Hybrid Systems: Computation and Control, Montreal, QC, Canada, 2019.

  3. E. M. Hahn, V. Hashemi, H. Hermanns, M. Lahijanian, and A. Turrini, “Interval Markov decision processes with multiple objectives: From Robust strategies to Pareto curves,” ACM Transactions on Modeling and Computer Simulation, vol. 29, no. 4, pp. 1–31, 2019.

  4. M. Wu et al., “Gaze-based Intention Anticipation over Driving Manoeuvres in Semi-Autonomous Vehicles,” in Proceedings of the IEEE International Conference on Intelligent Robots and Systems, 2019, pp. 6210–6216.

  5. F. Eiras, M. Lahijanian, and M. Kwiatkowska, “Correct-by-Construction Advanced Driver Assistance Systems based on a Cognitive Architecture,” in 2019 IEEE 2nd Connected and Automated Vehicles Symposium, 2019, pp. 1–7.

2018

  1. È. Pairet, J. D. Hernández, M. Lahijanian, and M. Carreras, “Uncertainty-based online mapping and motion planning for marine robotics guidance,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2018, pp. 2367–2374.

  2. H. Kress-Gazit, M. Lahijanian, and V. Raman, “Synthesis for robots: Guarantees and feedback for robot behavior,” Annual Review of Control, Robotics, and Autonomous Systems, vol. 1, pp. 211–236, 2018.

  3. S. Edelkamp, M. Lahijanian, D. Magazzeni, and E. Plaku, “Integrating temporal reasoning and Sampling-Based motion planning for multigoal problems with dynamics and time windows,” IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 3473–3480, 2018.

  4. M. Lahijanian et al., “Resource-performance tradeoff analysis for mobile robots,” IEEE Robotics and Automation Letters, vol. 3, no. 3, pp. 1840–1847, 2018.

  5. K. He, M. Lahijanian, L. E. Kavraki, and M. Y. Vardi, “Automated Abstraction of Manipulation Domains for Cost-Based Reactive Synthesis,” IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 285–292, 2018.

2017

  1. K. He, M. Lahijanian, L. E. Kavraki, and M. Y. Vardi, “Reactive Synthesis for Finite Tasks Under Resource Constraints,” in Int. Conf. on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada, 2017, pp. 5326–5332.

  2. E. M. Hahn, V. Hashemi, H. Hermanns, M. Lahijanian, and A. Turrini, “Multi-objective Robust Strategy Synthesis for Interval Markov Decision Processes,” in International Conference on Quantitative Evaluation of Systems, Berlin, Germany, 2017, pp. 207–223.

2016

  1. M. Lahijanian and M. Kwiatkowska, “Specification Revision for Markov Decision Processes with Optimal Trade-off,” in Proceedings of the IEEE 55th Conference on Decision and Control, 2016, pp. 7411–7418.

  2. M. Lahijanian, M. R. Maly, D. Fried, L. E. Kavraki, H. Kress-Gazit, and M. Y. Vardi, “Iterative Temporal Planning in Uncertain Environments With Partial Satisfaction Guarantees,” IEEE Transactions on Robotics, vol. 32, no. 3, pp. 538–599, May 2016.

  3. M. Lahijanian and M. Kwiatkowska, “Social Trust: a Major Challenge for the Future of Autonomous Systems,” in AAAI Fall Symposium on Cross-Disciplinary Challenges for Autonomous Systems, 2016.

2015

  1. R. Luna, M. Lahijanian, M. Moll, and L. E. Kavraki, “Asymptotically Optimal Stochastic Motion Planning with Temporal Goals,” in Algorithmic Foundations of Robotics XI - Selected Contributions of the Eleventh International Workshop on the Algorithmic Foundations of Robotics, WAFR, 2015, vol. 107, pp. 335–352.

  2. M. Lahijanian, S. B. Andersson, and C. Belta, “Formal Verification and Synthesis for Discrete-Time Stochastic Systems,” IEEE Transactions on Automatic Control, vol. 60, no. 8, pp. 2031–2045, Aug. 2015.

  3. J. Wang, X. C. Ding, M. Lahijanian, I. C. Paschalidis, and C. Belta, “Temporal logic motion control using actor-critic methods,” International Journal of Robotics Research, vol. 34, no. 10, pp. 1329–1344, Aug. 2015.

  4. K. He, M. Lahijanian, L. E. Kavraki, and M. Y. Vardi, “Towards Manipulation Planning with Temporal Logic Specifications,” in Proceedings of the IEEE International Conference on Robotics and Automation, Seattle, WA, 2015, pp. 346–352.

  5. M. Lahijanian, S. Almagor, D. Fried, L. E. Kavraki, and M. Y. Vardi, “This Time the Robot Settles for a Cost: A Quantitative Approach to Temporal Logic Planning with Partial Satisfaction,” in Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, Austin, TX, 2015, pp. 3664–3671.

2014

  1. R. Luna, M. Lahijanian, M. Moll, and L. E. Kavraki, “Optimal and Efficient Stochastic Motion Planning in Partially-Known Environments,” in The Twenty-Eighth AAAI Conference on Artificial Intelligence, Quebec City, Canada, 2014, pp. 2549–2555.

  2. R. Luna, M. Lahijanian, M. Moll, and L. E. Kavraki, “Fast Stochastic Motion Planning with Optimality Guarantees using Local Policy Reconfiguration,” in IEEE Conference on Robotics and Automation, Hong Kong, China, 2014, pp. 3013–3019.

  3. M. Lahijanian, L. E. Kavraki, and M. Y. Vardi, “A Sampling-Based Strategy Planner for Nondeterministic Hybrid Systems,” in IEEE Conference on Robotics and Automation, Hong Kong, China, 2014, pp. 3005–3012.

2013

  1. M. R. Maly, M. Lahijanian, L. E. Kavraki, H. Kress-Gazit, and M. Y. Vardi, “Iterative Temporal Motion Planning for Hybrid Systems in Partially Unknown Environments,” in International Conference on Hybrid Systems: Computation and Control, Philadelphia, PA, USA, 2013, pp. 353–362.

2012

  1. M. Lahijanian, S. B. Andersson, and C. Belta, “Approximate Markovian Abstractions for Linear Stochastic Systems,” in Proceedings of the IEEE Conference on Decision and Control, 2012, pp. 5966–5971.

  2. X. C. Ding, J. Wang, M. Lahijanian, I. C. Paschalidis, and C. Belta, “Temporal Logic Motion Control using Actor-Critic Methods,” in Proceedings of the IEEE International Conference on Robotics and Automation, Saint Paul, MN, 2012, pp. 4687–4692.

  3. M. Lahijanian, S. B. Andersson, and C. Belta, “Temporal Logic Motion Planning and Control With Probabilistic Satisfaction Guarantees,” IEEE Transactions on Robotics, vol. 28, no. 2, pp. 396–409, Apr. 2012.

2011

  1. I. Cizelj, X. C. D. Ding, M. Lahijanian, A. Pinto, and C. Belta, “Probabilistically safe vehicle control in a hostile environment,” in IFAC Proceedings Volumes, 2011, vol. 44, no. 1, pp. 11803–11808.

  2. R. Moazzez Estanjini, X. C. Ding, M. Lahijanian, J. Wang, C. A. Belta, and I. C. Paschalidis, “Least squares temporal difference actor-critic methods with applications to robot motion control,” in Proceedings of the IEEE Conference on Decision and Control, 2011, pp. 704–709.

  3. M. Lahijanian, S. B. Andersson, and C. Belta, “Control of Markov decision processes from PCTL specifications,” in Proceedings of the 2011 American Control Conference, 2011, pp. 311–316.

2010

  1. M. Lahijanian, J. Wasniewski, S. B. Andersson, and C. Belta, “Motion planning and control from temporal logic specifications with probabilistic satisfaction guarantees,” in International Conference on Robotics and Automation, Anchorage, Alaska, 2010, pp. 1050–4729.

2009

  1. M. Lahijanian, M. Kloetzer, S. Itani, C. Belta, and S. B. Andersson, “Automatic deployment of autonomous cars in a robotic urban-like environment (rule),” in IEEE International Conference on Robotics and Automation, 2009, pp. 2055–2060.

  2. M. Lahijanian, S. B. Andersson, and C. Belta, “A probabilistic approach for control of a stochastic system from LTL specifications,” in Proceedings of the IEEE Conference on Decision and Control, 2009, pp. 2236–2241.

2008

  1. S. B. Andersson, D. Hristu-Varsakelis, M. Lahijanian, and others, “Observers in language-based control,” Communications in Information & Systems, vol. 8, no. 2, pp. 85–106, 2008.