Publications

The following publications have been generated from research conducted under this grant.


  1. Lyapunov-stable neural-network control. Hongkai Dai, Benoit Landry, Lujie Yang, Marco Pavone, Russ Tedrake. Robotics: Science and Systems (RSS) 2021. Under Review.

  2. Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks. Apoorva Sharma, Navid Azizan, Marco Pavone. Conference on Uncertainty in Artificial Intelligence.

  3. Safe Active Dynamics Learning and Control: A Sequential Exploration-Exploitation Framework. Thomas Lew, Apoorva Sharma, James Harrison, Andrew Bylard, Marco Pavone. Robotics: Science and Systems 2021. Under Review.

  4. Adaptive-Control-Oriented Meta-Learning for Nonlinear Systems. Spencer M. Richards, Navid Azizan, Jean-Jacques E. Slotine, and Marco Pavone. Under Review.

  5. Nonlinear Control Using Neural Lyapunov-Barrier Functions and Robust Convex Optimization. Charles Dawson, Zengyi Qin, Sicun Gao, Chuchu Fan. Under Review.


  1. Control Synthesis with Heterogeneous Structure: Imitation-based Trajectory-feedback Control with Signal Temporal Logic Specifications. Karen Leung, Marco Pavone. Conference on Decision and Control. Under Review.

  2. Reachable Polyhedral Marching (RPM): A Safety Verification Algorithm for Robotic Systems with Deep Neural Network Components. Joseph A. Vincent, Mac Schwager. International Conference on Robotics and Automation 2021 (ICRA ).

  3. iMHS: An Incremental Multi-Hypothesis Smoother. Fan Jiang, Varun Agrawal, Russell Buchanan, Maurice Fallon, Frank Dellaert. IROS 2021. Under Review.

  4. SReachTools Kernel Module: Data-Driven Stochastic Reachability Using Hilbert Space Embeddings of Distributions. Adam J. Thorpe, Kendric R. Ortiz, Meeko M. K. Oishi. CDC 2021. Under Review.

  5. Stochastic Optimal Control via Hilbert Space Embeddings of Distributions. Adam J. Thorpe, Meeko M. K. Oishi. CDC 2021. Under Review.

  6. Learning Approximate Forward Reachable Sets Using Separating Kernels. Adam J. Thorpe, Kendric R. Ortiz, Meeko M. K. Oishi. Learning For Dynamics and Control (L4DC).

  7. Steering the State of Linear Stochastic Systems: A Constrained Minimum Principle Formulation. Ali Pakniyat and Panagiotis Tsiotras. IEEE American Control Conference, 2021 (accepted).

  8. Steering the State of Linear Stochastic Systems with Partial Observations. Ali Pakniyat and Panagiotis Tsiotras. IEEE Conference on Decision and Control, 2021 (submitted).

  9. Differentially Private Outlier Detection in Multivariate Gaussian Signals. Kwassi H. Degue, Karthik Gopalakrishnan, Max Z. Li, Hamsa Balakrishnan, Jerome Le Ny. in Proc. of the 2021 American Control Conference, 2021.

  10. Differentially Private Outlier Detection in Correlated Data. Kwassi H. Degue, Karthik Gopalakrishnan, Max Z. Li, Hamsa Balakrishnan. in Proc. IEEE Conf. on Decision and Control, 2021.

  11. Katz, Sydney M., Corso, Anthony L., Strong, Christopher A., Kochenderfer, Mykel J. "Verification of Image-based Neural Network Controllers Using Generative Models." arXiv preprint arXiv:2105.07091 (2021).

  12. ZoPE: A Fast Optimizer for ReLU Networks with Low-Dimensional Inputs. Christopher A. Strong, Sydney M. Katz, Anthony L. Corso, Mykel J. Kochenderfer. arXiv preprint arXiv:2106.05325 (2021).

  13. Throughput-Fairness Tradeoffs in Mobility Platforms. Arjun Balasingam, Karthik Gopalakrishnan, Radhika Mittal, Venkat Arun, Ahmed Saeed, Mohammad Alizadeh, Hamsa Balakrishnan, Hari Balakrishnan. Proceedings of the 19th International Conference on Mobile Systems, Applications, and Services. 2021.

  14. Backpropagation through signal temporal logic specifications: Infusing logical structure into gradient-based methods. Karen Leung, Nikos Aréchiga, Marco Pavone. Int. Journal of Robotics Research, 2021. Under Review.


  1. Analysis and Design of Uncertain Cyber-Physical Systems. Alessandro Pinto. Computation-Aware Algorithmic Design for Cyber-Physical Systems, Birkhauser, 2021 (submitted).


  1. CoCo: Online Mixed-Integer Control via Supervised Learning. Abhishek Cauligi, Preston Culbertson, Edward Schmerling, Mac Schwager, Bartolomeo Stellato, and M. Pavone. IEEE Robotics and Automation Letters, 2021. (Submitted)


  1. DiNNO: Distributed Neural Network Optimization for Multi-Robot Collaborative Learning. Javier Yu, Joseph A. Vincent, M. Schwager. IEEE Robotics and Automation Letters, 2021. (Submitted)


  1. Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functions. Charles Dawson, Zengyi Qin, Sicun Gao, Chuchu Fan. 5th Annual Conference on Robot Learning, 2021


  1. Sample-Efficient Safety Assurances using Conformal Prediction. Rachel Luo, Shengjia Zhao, Jonathan Kuck, Boris Ivanovic, Silvio Savarese, Marco Pavone. IEEE Conference on Robotics and Automation, 2021. (Submitted)


  1. Reactive and Safe Road User Simulations using Neural Barrier Certificates. Meng, Yue, Zengyi Qin, and Chuchu Fan. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021). IEEE, 2021.


  1. Learning Density Distribution of Reachable States for Autonomous Systems. Meng, Yue, Dawei Sun, Zeng Qiu, Md Tawhid Bin Waez, Chuchu Fan. Conference on Robot Learning. PMLR, 2021.


  1. Second-Order Sensitivity Analysis for Bilevel Optimization. Robert Dyro, Edward Schmerling, Marco Pavone. Preprint (Under Review), 2021.


  1. A Unified View of SDP-based Neural Network Verification through Completely Positive Programming. Robin A. Brown, Edward Schmerling, Navid Azizan, Marco Pavone. Preprint (Under Review), 2021.


  1. Distribution Steering for Discrete-Time Linear Systems with General Disturbances using Characteristic Functions. Vignesh Sivaramakrishnan, Joshua Pilipovsky, Meeko Oishi, Panagiotis Tsiotras. 2022 American Control Conference, IEEE, 2022.

  2. Verifying Inverse Model Neural Networks. Chelsea Sidrane, Sydney M. Katz, Anthony Corso, Mykel Kochenderfer. International Conference on Machine Learning, 2022.

  3. Data-Driven Chance Constrained Control using Kernel Distribution Embeddings. Adam J. Thorpe, Thomas Lew, Meeko M. K. Oishi, Marco Pavone. 4th Annual Learning for Dynamics & Control Conference (Under Review), 2022.

  4. Deep Binary Reinforcement Learning for Scalable Verification. Christopher Lazarus and Mykel J. Kochenderfer. International Conference on Intelligent Robots and Systems (IROS), 2022.

  5. Sample-Efficient Safety Assurances using Conformal Prediction. Rachel Luo, Shengjia Zhao, Jonathan Kuck, Boris Ivanovic, Silvio Savarese, Edward Schmerling, Marco Pavone. Workshop on Algorithmic Foundations of Robotics, 2022. (Submitted)