# Sample Efficient Learning of Path Following and Obstacle Avoidance Behavior for Quadrotors

## TL;DR

This paper introduces a sample-efficient imitation learning algorithm for quadrotor control that enables real-world training of neural network policies capable of path following and obstacle avoidance.

## Contribution

It presents a novel approach combining model predictive control with imitation learning to train neural policies efficiently on real quadrotors with limited data.

## Key findings

- Policy performs local obstacle avoidance of unseen obstacles
- Training requires only a small number of demonstrations
- Policy is computationally efficient for real-time control

## Abstract

In this paper 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 path following control algorithm with collision avoidance. Due to the generalization ability of neural networks, the resulting policy performs local collision avoidance of unseen obstacles 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.