Daniele Gammelli

According to our database, Daniele Gammelli authored at least 16 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Transformer-Based Model Predictive Control: Trajectory Optimization via Sequence Modeling.

Davide Celestini, Daniele Gammelli, Tommaso Guffanti, Simone D'Amico, Elisa Capello, Marco Pavone

IEEE Robotics Autom. Lett., November, 2024

Generalizable Spacecraft Trajectory Generation via Multimodal Learning with Transformers.

Davide Celestini, Amirhossein Afsharrad, Daniele Gammelli, Tommaso Guffanti, Gioele Zardini, Sanjay Lall, Elisa Capello, Simone D'Amico, Marco Pavone

CoRR, 2024

Offline Hierarchical Reinforcement Learning via Inverse Optimization.

Carolin Schmidt, Daniele Gammelli, James Harrison, Marco Pavone, Filipe Rodrigues

CoRR, 2024

Towards Robust Spacecraft Trajectory Optimization via Transformers.

Yuji Takubo, Tommaso Guffanti, Daniele Gammelli, Marco Pavone, Simone D'Amico

CoRR, 2024

Adapting a Foundation Model for Space-based Tasks.

Matthew Foutter, Praneet Bhoj, Rohan Sinha, Amine Elhafsi, Somrita Banerjee, Christopher Agia, Justin Kruger, Tommaso Guffanti, Daniele Gammelli, Simone D'Amico, Marco Pavone

CoRR, 2024

Real-Time Control of Electric Autonomous Mobility-on-Demand Systems via Graph Reinforcement Learning.

Aaryan Singhal, Daniele Gammelli, Justin Luke, Karthik Gopalakrishnan, Dominik Helmreich, Marco Pavone

Proceedings of the European Control Conference, 2024

Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning.

Carolin Schmidt, Daniele Gammelli, Francisco Câmara Pereira, Filipe Rodrigues

Proceedings of the European Control Conference, 2024

2023

Transformers for Trajectory Optimization with Application to Spacecraft Rendezvous.

Tommaso Guffanti, Daniele Gammelli, Simone D'Amico, Marco Pavone

CoRR, 2023

Graph Reinforcement Learning for Network Control via Bi-Level Optimization.

Daniele Gammelli, James Harrison, Kaidi Yang, Marco Pavone, Filipe Rodrigues, Francisco C. Pereira

Proceedings of the International Conference on Machine Learning, 2023

2022

Generalized multi-output Gaussian process censored regression.

Daniele Gammelli, Kasper Pryds Rolsted, Dario Pacino, Filipe Rodrigues

Pattern Recognit., 2022

Recurrent flow networks: A recurrent latent variable model for density estimation of urban mobility.

Daniele Gammelli, Filipe Rodrigues

Pattern Recognit., 2022

Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand.

Daniele Gammelli, Kaidi Yang, James Harrison, Filipe Rodrigues, Francisco C. Pereira, Marco Pavone

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

2021

Predictive and Prescriptive Performance of Bike-Sharing Demand Forecasts for Inventory Management.

Daniele Gammelli, Yihua Wang, Dennis Prak, Filipe Rodrigues, Stefan Minner, Francisco Câmara Pereira

CoRR, 2021

Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems.

Daniele Gammelli, Kaidi Yang, James Harrison, Filipe Rodrigues, Francisco C. Pereira, Marco Pavone

Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

2020

Recurrent Flow Networks: A Recurrent Latent Variable Model for Spatio-Temporal Density Modelling.

Daniele Gammelli, Filipe Rodrigues

CoRR, 2020

Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes.

Daniele Gammelli, Inon Peled, Filipe Rodrigues, Dario Pacino, Haci A. Kurtaran, Francisco C. Pereira

CoRR, 2020