ASL LabStanford University
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Daniele Gammelli

gammelli _at_ stanford _dot_ edu

Daniele Gammelli

Postdoctoral Scholar

Daniele Gammelli is a postdoctoral scholar in Stanford’s Autonomous Systems Lab, where he focuses on developing learning-based solutions that enable the deployment of future autonomous systems in complex environments, with an emphasis on large-scale robotic networks, mobility systems and autonomous spacecraft. He received his Ph.D. in Machine Learning and Mathematical Optimization at the Technical University of Denmark, where he developed ML-based solutions to analyze and control future Intelligent Transportation Systems.

More broadly, his research interests include deep reinforcement learning, generative models, graph neural networks, bayesian statistics, and control techniques leveraging these tools.

Beyond research, Daniele enjoys practicing soccer, going on trail runs, reading, and cooking.

Awards:

  • Kaj and Hermilla Ostenfeld’s Excellence Research Fund

Publications

18 publications · full lab bibliography

2026
  1. L. Schroth, D. Morton, A. Lahr, D. Gammelli, A. Carron, and M. Pavone, “Multi-Timescale Model Predictive Control for Slow-Fast Systems,” European Control Conference, 2026. (Submitted)
  2. 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)
  3. Y. Takubo, D. Gammelli, M. Pavone, and S. D'Amico, “Agile Tradespace Exploration for Space Rendezvous Mission Design via Transformers,” IEEE Aerospace Conference, 2026.
  4. L. Tresca, C. Schmidt, J. Harrison, F. Rodrigues, G. Zardini, D. Gammelli, and M. Pavone, “Robo-taxi Fleet Coordination at Scale via Reinforcement Learning,” IEEE Transactions on Control of Network Systems, 2026. (Submitted)
  5. Y. Takubo, A. Dwivedi, S. Ramkumar, L. Pabon, D. Gammelli, M. Pavone, and S. D'Amico, “Semantic Trajectory Generation for Goal-Oriented Spacecraft Rendezvous,” AIAA Scitech Forum, 2026.
2025
  1. D. Celestini, A. Afsharrad, D. Gammelli, T. Guffanti, G. Zardini, S. Lall, E. Capelli, S. D'Amico, and M. Pavone, “Generalizable Spacecraft Trajectory Generation via Multimodal Learning with Transformers,” American Control Conference, 2025.
  2. C. Schmidt, D. Gammelli, J. Harrison, M. Pavone, and F. Rodrigues, “Offline Hierarchical Reinforcement Learning via Inverse Optimization,” Int. Conf. on Learning Representations, 2025.
  3. X. Li, M. Alharbi, D. Gammelli, J. Harrison, F. Rodrigues, M. Schiffer, M. Pavone, E. Frazzoli, J. Zhao, and G. Zardini, “Reproducibility in the Control of Autonomous Mobility-on-Demand Systems,” IEEE Transactions on Robotics, 2025. (Submitted)
  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. Y. Takubo, T. Guffanti, D. Gammelli, M. Pavone, and S. D'Amico, “Towards Robust Spacecraft Trajectory Optimization via Transformers,” IEEE Aerospace Conference, 2025.
  6. 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. A. Singhal, D. Gammelli, J. Luke, K. Gopalakrishnan, D. Helmreich, and M. Pavone, “Real-time Control of Electric Autonomous Mobility-on-Demand Systems via Graph Reinforcement Learning,” European Control Conference, 2024.
  2. D. Celestini, D. Gammelli, T. Guffanti, S. D'Amico, E. Capelli, and M. Pavone, “Transformer-based Model Predictive Control: Trajectory Optimization via Sequence Modeling,” IEEE Robotics and Automation Letters, vol. 9, no. 11, pp. 9280--9827, 2024.
  3. T. Guffanti, D. Gammelli, S. D'Amico, and M. Pavone, “Transformers for Trajectory Optimization with Application to Spacecraft Rendezvous,” IEEE Aerospace Conference, 2024.
2023
  1. D. Gammelli, J. Harrison, K. Yang, M. Pavone, F. Rodrigues, and F. C. Pereira, “Graph Reinforcement Learning for Network Control via Bi-Level Optimization,” Int. Conf. on Machine Learning, 2023.
2022
  1. D. Gammelli, K. Yang, J. Harrison, F. Rodrigues, F. Pereira, and M. Pavone, “Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand,” ACM Int. Conf. on Knowledge Discovery and Data Mining, 2022.
  2. D. Gammelli, J. Harrison, K. Yang, M. Pavone, F. Rodrigues, and P. C. Francisco, “Graph Reinforcement Learning for Network Control via Bi-Level Optimization,” Learning on Graphs Conference, 2022.
2021
  1. D. Gammelli, K. Yang, J. Harrison, F. Rodrigues, F. C. Pereira, and M. Pavone, “Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems,” Proc. IEEE Conf. on Decision and Control, 2021.