# Vision-based Control of a Quadrotor in User Proximity: Mediated vs End-to-End Learning Approaches

arXiv: https://arxiv.org/abs/1809.08881 · HTML: https://mlsanity.com/paper/1809.08881/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2018.

## Built on by (most cited first, 1 of 1)

- [Training Lightweight CNNs for Human-Nanodrone Proximity Interaction from Small Datasets using Background Randomization](https://mlsanity.com/paper/2110.14491/) (2021)

## Abstract (arXiv)

We consider the task of controlling a quadrotor to hover in front of a freely moving user, using input data from an onboard camera. On this specific task we compare two widespread learning paradigms: a mediated approach, which learns an high-level state from the input and then uses it for deriving control signals; and an end-to-end approach, which skips high-level state estimation altogether. We show that despite their fundamental difference, both approaches yield equivalent performance on this task. We finally qualitatively analyze the behavior of a quadrotor implementing such approaches.