Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Gammelli, Daniele"'
Autor:
Celestini, Davide, Afsharrad, Amirhossein, Gammelli, Daniele, Guffanti, Tommaso, Zardini, Gioele, Lall, Sanjay, Capello, Elisa, D'Amico, Simone, Pavone, Marco
Effective trajectory generation is essential for reliable on-board spacecraft autonomy. Among other approaches, learning-based warm-starting represents an appealing paradigm for solving the trajectory generation problem, effectively combining the ben
Externí odkaz:
http://arxiv.org/abs/2410.11723
Hierarchical policies enable strong performance in many sequential decision-making problems, such as those with high-dimensional action spaces, those requiring long-horizon planning, and settings with sparse rewards. However, learning hierarchical po
Externí odkaz:
http://arxiv.org/abs/2410.07933
Future multi-spacecraft missions require robust autonomous trajectory optimization capabilities to ensure safe and efficient rendezvous operations. This capability hinges on solving non-convex optimal control problems in real time, although tradition
Externí odkaz:
http://arxiv.org/abs/2410.05585
Autor:
Foutter, Matthew, Bhoj, Praneet, Sinha, Rohan, Elhafsi, Amine, Banerjee, Somrita, Agia, Christopher, Kruger, Justin, Guffanti, Tommaso, Gammelli, Daniele, D'Amico, Simone, Pavone, Marco
Foundation models, e.g., large language models, possess attributes of intelligence which offer promise to endow a robot with the contextual understanding necessary to navigate complex, unstructured tasks in the wild. In the future of space robotics,
Externí odkaz:
http://arxiv.org/abs/2408.05924
Real-time Control of Electric Autonomous Mobility-on-Demand Systems via Graph Reinforcement Learning
Autor:
Singhal, Aaryan, Gammelli, Daniele, Luke, Justin, Gopalakrishnan, Karthik, Helmreich, Dominik, Pavone, Marco
Publikováno v:
2024 European Control Conference (ECC), pp. 1407-1414, 2024
Operators of Electric Autonomous Mobility-on-Demand (E-AMoD) fleets need to make several real-time decisions such as matching available vehicles to ride requests, rebalancing idle vehicles to areas of high demand, and charging vehicles to ensure suff
Externí odkaz:
http://arxiv.org/abs/2311.05780
Reliable and efficient trajectory optimization methods are a fundamental need for autonomous dynamical systems, effectively enabling applications including rocket landing, hypersonic reentry, spacecraft rendezvous, and docking. Within such safety-cri
Externí odkaz:
http://arxiv.org/abs/2310.13831
Autor:
Gammelli, Daniele, Harrison, James, Yang, Kaidi, Pavone, Marco, Rodrigues, Filipe, Pereira, Francisco C.
Optimization problems over dynamic networks have been extensively studied and widely used in the past decades to formulate numerous real-world problems. However, (1) traditional optimization-based approaches do not scale to large networks, and (2) th
Externí odkaz:
http://arxiv.org/abs/2305.09129
Autonomous Mobility-on-Demand (AMoD) systems are an evolving mode of transportation in which a centrally coordinated fleet of self-driving vehicles dynamically serves travel requests. The control of these systems is typically formulated as a large ne
Externí odkaz:
http://arxiv.org/abs/2302.14833
Autor:
Gammelli, Daniele, Yang, Kaidi, Harrison, James, Rodrigues, Filipe, Pereira, Francisco C., Pavone, Marco
Autonomous Mobility-on-Demand (AMoD) systems represent an attractive alternative to existing transportation paradigms, currently challenged by urbanization and increasing travel needs. By centrally controlling a fleet of self-driving vehicles, these
Externí odkaz:
http://arxiv.org/abs/2202.07147
Autor:
Gammelli, Daniele, Wang, Yihua, Prak, Dennis, Rodrigues, Filipe, Minner, Stefan, Pereira, Francisco Camara
Bike-sharing systems are a rapidly developing mode of transportation and provide an efficient alternative to passive, motorized personal mobility. The asymmetric nature of bike demand causes the need for rebalancing bike stations, which is typically
Externí odkaz:
http://arxiv.org/abs/2108.00858