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Artificial Intelligence

Deep Learning with Domain Randomization

Learn how to train any robot to recognize an object and pinpoint its 3D location with only an RGB camera and a lot of training with Keras.

Course Overview

This course is intended for the people that want to learn about deep learning using Keras

In this case, we use a very interesting approach to learning, which is Environment Randomization. This method exploits the versatility of environment generation in simulations to train a robot in a way that results in a model that is very robust, no matter the lighting conditions. It also makes the transition from simulated learning to reality much smoother and quicker. Learn through hands-on experience how to train a robot for 3D object recognition using random environments.

Keras will be the cornerstone of this system and you will learn all the necessary skills to generate training data, convert it to a database, train a MobileNetV2 model, retrain it, and make predictions with it.

The final project is the training of a garbage-picking robot, from training data generation to the final garbage detection and picking program. Dive into the fantastic world of DeepLearning with Keras now!

What You Will Learn

How to use Keras in a basic way

How to train a DeepNeuralNetwork by using a Gazebo Simulation

How to work with ROS+Gazebo+Keras in tandem

How Random Environment generation works in Gazebo

You are going to program the following robots:

Fetch Robot

Fetch Robot

Garbage Collector

Garbage Collector

Learning Path
Unit 0

Quick Demo

Unit 1

Simple Step-By-Step Guide

Unit 2

Exercises For XY Motion Spam

Unit 3

Exercises for Spam and a Distractor

Unit 4

Exercises with Distractor and Random Env

Unit 5

Microproject Garbage Collector

Unit 6

I'm finished, now what?