Smart Robots for Developers Training Course

Section 01

Day 01
Introduction

• What Makes a Smart Robot Smart?

Physical vs Virtual Smart Robots

• Smart Robots, Smart Machines, Sentient Machines and Robotic Process Automation (RPA), etc.

The Role of Artificial Intelligence (AI) in Smart Robots

• Beyond "if-then-else" and the learning machine
• The algorithms behind AI
• AI in Smart Robots: machine learning, computer vision, natural language processing (NLP), etc.
• Cognitive robotics

The Role of Big Data in Smart Robots

• Decision-making based on data and patterns

The Cloud and Smart Robots

• Linking robotics with IT
• Building more functional robots that access more information and collaborate

Case Study: Mechanical Smart Robots

• Industrial Smart Robots
  • Baxter
• Personal Service Robots
  • Domestic robots that assist the elderly, smart self-driving cars
• Professional Service Robots
  • Agricultural robots in diary operations

Hardware components of a Smart Robot

• Motors, sensors, microcontrollers, cameras, etc.

Common Elements of Smart Robots

• Machine vision, voice recognition, speech synthesis, proximity sensing, pressure sensing, etc.

Development Frameworks for Programming a Smart Robot

• Open source and commercial frameworks
• Robot Operating System (ROS)
  • Architecture: workspace, topics, messages, services, nodes, actionlibs, tools, etc.

Languages for Programming a Smart Robot

• C++ for low level controlling
• Python for orchestration
• Programming ROS nodes in Python and C ++
• Other languages

Tools for Simulating a Physical Smart Robot

• Commercial and open source 3D simulation and visualization software

Preparing the Development Environment

• Software installation and setup
• Useful packages and utilities

Day 02
Programming the Smart Robot

• Programming a node in Python and C ++
• Understanding ROS node
• Messages and topics in ROS
• Publication / subscription paradigm
• Project: Bump & Go with real robot
• Troubleshooting
• Simulation of robots with Gazebo / ROS
• Frames in ROS and reference changes
• 2D information processing of cameras with OpenCV
• Information processing of a laser
• Project: Safe tracking of objects by color
• Troubleshooting

Day 03
Programming the Smart Robot (Continued...)

• Services in ROS
• 3D information processing of RGB-D sensors with PCL
• Maps and Navigation with ROS
• Project: Search for objects in the environment
• Troubleshooting

Section 02

Day 04
Programming the Smart Robot (Continued...)

• ActionLib
• Speech Recognition and Speech Generation
• Controlling robotic arms with MoveIt!
• Controlling robotic neck for active vision
• Project: Search and collection of objects
• Troubleshooting

Testing Your Smart Robot

• Unit testing

Day 05
Extending a Smart Robot's Capabilities with Deep Learning

• Perception -- vision, audio, and haptics
• Knowledge representation
• Voice recognition through NLP (natural language processing)
• Computer vision

Crash Course in Deep Learning

• Artificial Neural Networks (ANNs)
• Artificial Neural Networks vs. Biological Neural Networks
• Feedforward Neural Networks
• Activation Functions
• Training Artificial Neural Networks

Day 06
Crash Course in Deep Learning (Continued...)

• Deep Learning Models
  • Convolutional Networks and Recurrent Networks
• Convolutional Neural Networks (CNNs or ConvNets)
  •  Convolution Layer
  •  Pooling Layer
  •  Convolutional Neural Networks Architecture

Section 03

Day 07
Crash Course in Deep Learning (Continued...)

• Recurrent Neural Networks (RNN)
  • Training an RNN
  • Stabilizing gradients during training
  • Long short-term memory networks
• Deep Learning Platforms and Software Libraries
  • Deep Learning in ROS

Day 08
Using Big Data in Your Smart Robot

• Big data concepts
• Approaches to data analysis
• Big Data tooling
• Recognizing patterns in the data
• Exercise: NLP and Computer Vision on large data sets

Day 09
Using Big Data in Your Smart Robot (Continued...)

• Distributed processing of large data sets
• Coexistence and cross-fertilization of Big Data and Robotics
• The Smart Robot as a generator of data
  • Range measuring sensors, position, visual, tactile sensors, and other modalities
• Making sense of sensory data (sense-plan-act loop)
• Exercise: Capturing streaming data

Section 04

Day 10
Programming an Autonomous Deep Learning Smart Robot

• Deep Learning robot components
• Setting up the robot simulator
• Running a CUDA-accelerated neural network with Cafe
• Troubleshooting

Day 11
Programming an Autonomous Deep Learning Smart Robot (Continued...)

• Recognizing objects in photographs or video streams
• Enabling computer vision with OpenCV
• Troubleshooting

Day 12
Data Analytics

• Using the Smart Robot to collect and organize new data

Building a Smart Robot Collaboratively

Deploying Your Smart Robot on Physical Hardware

Monitoring and Servicing Smart Robots in the Field

Securing Your Robot

• Preventing unauthorized tampering
• Preventing hackers from viewing and stealing sensitive business data (credit card, employee information, etc.)

Joining to the Robotics Community

Future Outlook for Smart Robots

Closing Remarks