Simultaneous Learning of State-to-State Minimum-Time Planning and Control

Swati Dantu, Robert Pěnička, Martin Saska

2025

Abstract

This paper tackles the challenge of learning a generalizable minimum-time flight policy for UAVs, capable of navigating between arbitrary start and goal states while balancing agile flight and stable hovering. Traditional approaches, particularly in autonomous drone racing, achieve impressive speeds and agility but are constrained to predefined track layouts, limiting real-world applicability. To address this, we propose a reinforcement learning-based framework that simultaneously learns state-to-state minimum-time planning and control and generalizes to arbitrary state-to-state flights. Our approach leverages Point Mass Model (PMM) trajectories as proxy rewards to approximate the true optimal flight objective and employs curriculum learning to scale the training process efficiently and to achieve generalization. We validate our method through simulation experiments, comparing it against Nonlinear Model Predictive Control (NMPC) tracking PMM-generated trajectories and conducting ablation studies to assess the impact of curriculum learning. Finally, real-world experiments confirm the robustness of our learned policy in outdoor environments, demonstrating its ability to generalize and operate on a small ARM-based single-board computer.

Paper Citation

in Harvard Style

Dantu S., Pěnička R. and Saska M. (2025). Simultaneous Learning of State-to-State Minimum-Time Planning and Control. In Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO; ISBN 978-989-758-770-2, SciTePress, pages 283-291. DOI: 10.5220/0013716800003982

in Bibtex Style

@conference{icinco25,
author={Swati Dantu and Robert Pěnička and Martin Saska},
title={Simultaneous Learning of State-to-State Minimum-Time Planning and Control},
booktitle={Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO},
year={2025},
pages={283-291},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013716800003982},
isbn={978-989-758-770-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO
TI - Simultaneous Learning of State-to-State Minimum-Time Planning and Control
SN - 978-989-758-770-2
AU - Dantu S.
AU - Pěnička R.
AU - Saska M.
PY - 2025
SP - 283
EP - 291
DO - 10.5220/0013716800003982
PB - SciTePress