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Dario Izzo

2 accepted papers

2024

End-to-end Reinforcement Learning for Time-Optimal Quadcopter Flight

ICRA 2024poster

Aggressive time-optimal control of quadcopters poses a significant challenge in the field of robotics. The state-of-the-art approach leverages reinforcement learning (RL) to train optimal neural policies. However, a critical hurdle is the sim-to-real gap, often addressed by employing a robust inner…

Cited by 10SourceScholar
2020

Aggressive Online Control of a Quadrotor via Deep Network Representations of Optimality Principles

ICRA 2020poster

Optimal control holds great potential to improve a variety of robotic applications. The application of optimal control on-board limited platforms has been severely hindered by the large computational requirements of current state of the art implementations. In this work, we make use of a deep neural…

Cited by 40SourceScholar