# Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learning

## TL;DR

This paper demonstrates the first use of model-based reinforcement learning to control a small quadrotor for hovering using only onboard sensors and minimal training data, enabling rapid, platform-specific low-level control.

## Contribution

It introduces a novel MBRL approach for quadrotor control that requires no prior dynamics knowledge and minimal data, achieving hover with limited training.

## Key findings

- Quadrotor achieved up to 6 seconds of hover.
- Training required only 3 minutes of data.
- Controller operates at less than 50Hz.

## Abstract

Designing effective low-level robot controllers often entail platform-specific implementations that require manual heuristic parameter tuning, significant system knowledge, or long design times. With the rising number of robotic and mechatronic systems deployed across areas ranging from industrial automation to intelligent toys, the need for a general approach to generating low-level controllers is increasing. To address the challenge of rapidly generating low-level controllers, we argue for using model-based reinforcement learning (MBRL) trained on relatively small amounts of automatically generated (i.e., without system simulation) data. In this paper, we explore the capabilities of MBRL on a Crazyflie centimeter-scale quadrotor with rapid dynamics to predict and control at <50Hz. To our knowledge, this is the first use of MBRL for controlled hover of a quadrotor using only on-board sensors, direct motor input signals, and no initial dynamics knowledge. Our controller leverages rapid simulation of a neural network forward dynamics model on a GPU-enabled base station, which then transmits the best current action to the quadrotor firmware via radio. In our experiments, the quadrotor achieved hovering capability of up to 6 seconds with 3 minutes of experimental training data.