Sample efficient learning of path following and obstacle avoidance behavior for Quadrotors

Stefan Stevsic, Tobias Nägeli, Javier Alonso Mora, Otmar Hilliges

Research output: Contribution to journalArticleScientificpeer-review

16 Citations (Scopus)
63 Downloads (Pure)


In this letter, we propose an algorithm for the training of neural network control policies for quadrotors. The learned control policy computes control commands directly from sensor inputs and is, hence, computationally efficient. An imitation learning algorithm produces a policy that reproduces the behavior of a supervisor. The supervisor provides demonstrations of path following and collision avoidance maneuvers. Due to the generalization ability of neural networks, the resulting policy performs local collision avoidance, while following a global reference path. The algorithm uses a time-free model-predictive path-following controller as a supervisor. The controller generates demonstrations by following few example paths. This enables an easy-to-implement learning algorithm that is robust to errors of the model used in the model-predictive controller. The policy is trained on the real quadrotor, which requires collision-free exploration around the example path. An adapted version of the supervisor is used to enable exploration. Thus, the policy can be trained from a relatively small number of examples on the real quadrotor, making the training sample efficient
Original languageEnglish
Pages (from-to)3852-3859
JournalIEEE Robotics and Automation Letters
Issue number4
Publication statusPublished - 2018

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Collision avoidance
  • deep learning in robotics and automation


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