ASL LabStanford University
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Matt Foutter

mfoutter _at_ stanford _dot_ edu

Matt Foutter

PhD Student

Matt is a PhD candidate in the Department of Mechanical Engineering. Matt's research interests lie at the intersection of machine learning and robotics with the goal of enabling an autonomous robot to safely navigate in an unfamiliar environment. Specifically, he is interested in developing methodologies that monitor a robot's operation at deployment - preemptively catching failure modes and enacting safety preserving actions.

Prior to joining Stanford, Matt graduated summa cum laude from the University of Michigan, Ann Arbor, with a B.S.E in Aerospace Engineering and minor in Computer Science. There, he conducted research in classical control techniques with highly flexible wings under Prof. Carlos Cesnik in the Active Aeroelasticity and Structures Research Laboratory. Also, as an undergraduate, he raced a solar car 1,800 miles across Australia and interned at SpaceX and MIT Lincoln Lab.

Outside of the lab, Matt enjoys his free time by reading and playing multiple sports including basketball and table tennis.

Publications

8 publications · full lab bibliography

2026
  1. M. Foutter, M. Cercola, L. Wild, Y. Wang, M. Li, D. Gammelli, and M. Pavone, “Do Vision-Language-Action Models Mean What They Say? On the Role of Faithfulness in Embodied Reasoning,” 2026. (Submitted)
2025
  1. L. Li, Z. Fan, W. Cong, X. Liu, Y. Yin, M. Foutter, P. Pan, C. You, Y. Wang, Z. Wang, Y. Zhao, M. Pavone, and Y. Wei, “Martian World Model: Controllable Video Synthesis with Physically Accurate 3D Reconstructions,” Conf. on Neural Information Processing Systems, 2025.
  2. J. Kwok, C. Agia, R. Sinha, M. Foutter, S. Li, I. Stoica, A. Mirhoseini, and M. Pavone, “RoboMonkey: Scaling Test-Time Sampling and Verification for Vision-Language-Action Models,” Conf. on Robot Learning, 2025. (In Press)
  3. 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.
  4. M. Foutter, D. Gammelli, J. Kruger, E. Foss, P. Bhoj, T. Guffanti, S. D'Amico, and M. Pavone, “Space-LLaVA: a Vision-Language Model Adapted to Extraterrestrial Applications,” IEEE Aerospace Conference, 2025.
  5. M. P. Ronecker, M. Foutter, A. Elhafsi, D. Gammelli, I. Barakaiev, M. Pavone, and D. Watzenig, “Vision Foundation Model Embedding-based Semantic Anomaly Detection,” Proc. IEEE Conf. on Robotics and Automation: Workshop Safe-VLM, 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.
2023
  1. M. Foutter, R. Sinha, S. Banerjee, and M. Pavone, “Self-Supervised Model Generalization using Out-of-Distribution Detection,” Conf. on Robot Learning - Workshop on Out-of-Distribution Generalization in Robotics, 2023.