ASL LabStanford University
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Edward Schmerling

schmrlng _at_ stanford _dot_ edu

Edward Schmerling

Alumni · now at NVIDIA Research

Ed Schmerling is a roboticist pursuing a Ph.D. with the Institute for Computational & Mathematical Engineering at Stanford University. He received a B.S. in Mathematics (with Honors) and Physics from Stanford in 2010.

Ed's research interests span questions in robot motion planning and decision making under uncertainty -— how to model a robot's interaction with its (possibly-sentient) environment, how to efficiently plan actions by closing the loop on only the most relevant data to the task at hand, how to quantify/certify the performance of these algorithms.

In a previous life, Ed was a captain and coach of the Stanford Prison Experiment, the men's ultimate frisbee B-team and a force to be feared in regional competition. Nowadays he's hung up the cleats unless rival labs are looking to further their higher education.

Awards:

  • Qualcomm Innovation Fellowship (2015)

Publications

44 publications · full lab bibliography

2025
  1. R. Dyro, M. Foutter, R. Li, L. Di Lillo, E. Schmerling, X. Zhou, and M. Pavone, “Realistic Extreme Behavior Generation for Improved AV Testing,” Proc. IEEE Conf. on Robotics and Automation, 2025.
2024
  1. R. Sinha, A. Elhafsi, C. Agia, M. Foutter, E. Schmerling, and M. Pavone, “Real-Time Anomaly Detection and Planning with Large Language Models,” Robotics: Science and Systems, 2024.
  2. A. Hindy, R. Luo, S. Banerjee, J. Kuck, E. Schmerling, and M. Pavone, “Diagnostic Runtime Monitoring with Martingales,” 2024. (Submitted)
  3. R. Luo, R. Sinha, Y. Sun, A. Hindy, S. Zhao, S. Savarese, E. Schmerling, and M. Pavone, “Online Distribution Shift Detection via Recency Prediction,” Proc. IEEE Conf. on Robotics and Automation, 2024.
  4. L. Paparusso, S. Kousik, E. Schmerling, F. Braghin, and M. Pavone, “ZAPP! Zonotope Agreement of Prediction and Planning for Continuous-Time Collision Avoidance with Discrete-Time Dynamics,” Proc. IEEE Conf. on Robotics and Automation, 2024. (Submitted)
2023
  1. A. Elhafsi, R. Sinha, C. Agia, E. Schmerling, I. A. D. Nesnas, and M. Pavone, “Semantic Anomaly Detection with Large Language Models,” Autonomous Robots, vol. 47, no. 8, pp. 1035--1055, 2023.
  2. J. Alora, M. Cenedese, E. Schmerling, G. Haller, and M. Pavone, “Practical Deployment of Spectral Submanifold Reduction for Optimal Control of High-Dimensional Systems,” IFAC World Congress, 2023.
  3. S. Newdick, N. Ongole, T. G. Chen, E. Schmerling, M. R. Cutkosky, and M. Pavone, “Motion Planning for a Climbing Robot with Stochastic Grasps,” Proc. IEEE Conf. on Robotics and Automation, 2023.
  4. S. Banerjee, A. Sharma, E. Schmerling, M. Spolaor, M. Nemerouf, and M. Pavone, “Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace Applications,” IEEE Aerospace Conference, 2023.
  5. S. Newdick, T. G. Chen, B. Hockman, E. Schmerling, M. R. Cutkosky, and M. Pavone, “Designing ReachBot: System Design Process with a Case Study of a Martian Lava Tube Mission,” IEEE Aerospace Conference, 2023.
  6. R. Sinha, E. Schmerling, and M. Pavone, “Closing the Loop on Runtime Monitors with Fallback-Safe MPC,” Proc. IEEE Conf. on Decision and Control, 2023.
  7. J. Alora, M. Cenedese, E. Schmerling, G. Haller, and M. Pavone, “Data-Driven Spectral Submanifold Reduction for Nonlinear Optimal Control of High-Dimensional Robots,” Proc. IEEE Conf. on Robotics and Automation, 2023.
  8. J. Alora, L. Pabon, J. Köhler, M. Cenedese, E. Schmerling, Z. M. N., G. Haller, and M. Pavone, “Robust Nonlinear Reduced-Order Model Predictive Control,” Proc. IEEE Conf. on Decision and Control, 2023.
  9. R. Luo, S. Zhao, J. Kuck, B. Ivanovic, S. Savarese, E. Schmerling, and M. Pavone, “Sample-Efficient Safety Assurances using Conformal Prediction,” Int. Journal of Robotics Research, 2023.
2022
  1. A. Wu, T. Lew, K. Solovey, E. Schmerling, and M. Pavone, “Robust-RRT: Probabilistically-Complete Motion Planning for Uncertain Nonlinear Systems,” Int. Symp. on Robotics Research, 2022.
  2. R. Luo, S. Zhao, J. Kuck, B. Ivanovic, S. Savarese, E. Schmerling, and M. Pavone, “Sample-Efficient Safety Assurances using Conformal Prediction,” Workshop on Algorithmic Foundations of Robotics, 2022.
  3. R. Sinha, S. Sharma, S. Banerjee, T. Lew, R. Luo, S. M. Richards, Y. Sun, E. Schmerling, and M. Pavone, “A System-Level View on Out-of-Distribution Data in Robotics,” 2022.
  4. R. Brown, E. Schmerling, N. Azizan, and M. Pavone, “A Unified View of SDP-based Neural Network Verification through Completely Positive Programming,” Int. Conf. on Artificial Intelligence and Statistics, 2022.
  5. A. Cauligi, P. Culbertson, E. Schmerling, M. Schwager, B. Stellato, and M. Pavone, “CoCo: Online Mixed-Integer Control via Supervised Learning,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 1447--1454, 2022.
  6. R. Luo, A. Bhatnagar, H. Wang, C. Xiong, S. Savarese, Y. Bai, S. Zhao, S. Ermon, E. Schmerling, and M. Pavone, “Local Calibration: Metrics and Recalibration,” Proc. Conf. on Uncertainty in Artificial Intelligence, 2022.
  7. R. Dyro, E. Schmerling, N. Arechiga, and M. Pavone, “Second-Order Sensitivity Analysis for Bilevel Optimization,” Int. Conf. on Artificial Intelligence and Statistics, 2022.
  8. F. Mahlknecht, J. Alora, S. Jain, E. Schmerling, R. Bonalli, G. Haller, and M. Pavone, “Using Spectral Submanifolds for Nonlinear Periodic Control,” Proc. IEEE Conf. on Decision and Control, 2022.
2021
  1. B. Ivanovic, K. Leung, E. Schmerling, and M. Pavone, “Multimodal Deep Generative Models for Trajectory Prediction: A Conditional Variational Autoencoder Approach,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 295--302, 2021.
2020
  1. K. Solovey, L. Janson, E. Schmerling, E. Frazzoli, and M. Pavone, “Revisiting the Asymptotic Optimality of RRT*,” Proc. IEEE Conf. on Robotics and Automation, 2020.
  2. K. Leung, E. Schmerling, M. Zhang, M. Chen, J. Talbot, J. C. Gerdes, and M. Pavone, “On Infusing Reachability-Based Safety Assurance within Planning Frameworks for Human-Robot Vehicle Interactions,” Int. Journal of Robotics Research, vol. 39, no. 10--11, pp. 1326--1345, 2020.
2019
  1. E. Schmerling, “Multimodal Modeling and Uncertainty Quantification for Robot Planning and Decision Making,” Ph.D. dissertation, Stanford University, Inst. for Computational & Mathematical Engineering, 2019.
  2. E. Schmerling and M. Pavone, “Kinodynamic Planning,” Encyclopedia of Robotics, 2019.
2018
  1. B. Ivanovic, E. Schmerling, K. Leung, and M. Pavone, “Generative Modeling of Multimodal Multi-Human Behavior,” IEEE/RSJ Int. Conf. on Intelligent Robots & Systems, 2018.
  2. E. Schmerling, K. Leung, W. Vollprecht, and M. Pavone, “Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction,” Proc. IEEE Conf. on Robotics and Automation, 2018.
  3. K. Leung, E. Schmerling, M. Chen, J. Talbot, J. C. Gerdes, and M. Pavone, “On Infusing Reachability-Based Safety Assurance within Probabilistic Planning Frameworks for Human-Robot Vehicle Interactions,” Int. Symp. on Experimental Robotics, 2018.
2017
  1. B. Ichter, B. Landry, E. Schmerling, and M. Pavone, “Perception-Aware Motion Planning via Multiobjective Search on GPUs,” Int. Symp. on Robotics Research, 2017.
  2. E. Schmerling and M. Pavone, “Evaluating Trajectory Collision Probability through Adaptive Importance Sampling for Safe Motion Planning,” Robotics: Science and Systems, 2017.
  3. B. Ichter, E. Schmerling, A. Agha-mohammadi, and M. Pavone, “Real-Time Stochastic Kinodynamic Motion Planning via Multiobjective Search on GPUs,” Proc. IEEE Conf. on Robotics and Automation, 2017.
  4. B. Ichter, E. Schmerling, and M. Pavone, “Group Marching Tree: Sampling-Based Approximately Optimal Motion Planning on GPUs,” IEEE Int. Conf. on Robotic Computing, 2017.
  5. J. A. Starek, E. Schmerling, G. D. Maher, B. W. Barbee, and M. Pavone, “Fast, Safe, Propellant-Efficient Spacecraft Motion Planning Under Clohessy-Wiltshire-Hill Dynamics,” AIAA Journal of Guidance, Control, and Dynamics, vol. 40, no. 2, pp. 418--438, 2017.
2016
  1. J. A. Starek, E. Schmerling, G. D. Maher, B. W. Barbee, and M. Pavone, “Real-Time, Propellant-Optimized Spacecraft Motion Planning under Clohessy-Wiltshire-Hill Dynamics,” IEEE Aerospace Conference, 2016.
  2. K. Leung, E. Schmerling, and M. Pavone, “Distributional Prediction of Human Driving Behaviours using Mixture Density Networks,” Stanford University, 2016.
2015
  1. Z. Zhu, E. Schmerling, and M. Pavone, “A Convex Optimization Approach to Smooth Trajectories for Motion Planning with Car-Like Robots,” Proc. IEEE Conf. on Decision and Control, 2015.
  2. E. Schmerling, L. Janson, and M. Pavone, “Optimal Sampling-Based Motion Planning under Differential Constraints: the Drift Case with Linear Affine Dynamics,” Proc. IEEE Conf. on Decision and Control, 2015.
  3. J. A. Starek, J. V. Gomez, E. Schmerling, L. Janson, L. Moreno, and M. Pavone, “An Asymptotically-Optimal Sampling-Based Algorithm for Bi-directional Motion Planning,” IEEE/RSJ Int. Conf. on Intelligent Robots & Systems, 2015.
  4. L. Janson, E. Schmerling, and M. Pavone, “Monte Carlo Motion Planning for Robot Trajectory Optimization Under Uncertainty,” Int. Symp. on Robotics Research, 2015.
  5. S. Singh, E. Schmerling, and M. Pavone, “Decentralized Algorithms for 3D Symmetric Formations in Robotic Networks - A Contraction Theory Approach,” Proc. IEEE Conf. on Robotics and Automation, 2015.
  6. E. Schmerling, L. Janson, and M. Pavone, “Optimal Sampling-Based Motion Planning under Differential Constraints: the Driftless Case,” Proc. IEEE Conf. on Robotics and Automation, 2015.
  7. L. Janson, E. Schmerling, A. Clark, and M. Pavone, “Fast Marching Tree: A Fast Marching Sampling-Based Method for Optimal Motion Planning in Many Dimensions,” Int. Journal of Robotics Research, vol. 34, no. 7, pp. 883--921, 2015.