AI+ Robotics™

AI+ Robotics™

Build the Future with Smart Automation

Certificate Code: AT-420

About This Course

  • AI-Driven Robotics: Apply AI in Deep Learning, Reinforcement Learning, and smart automation
  • Real-World Systems: Work with autonomous systems and intelligent agents
  • Ethics & Innovation: Learn industry-aligned practices and innovation strategies
  • Hands-On Projects: Gain experience designing, optimising, and deploying robotics solutions

Certificate Overview

Included

Instructor-led OR Self-paced course + Official exam + Digital badge

Duration

  • Instructor-Led: 5 days (live or virtual) 
  • Self-Paced: 40 hours of content

Prerequisites

Basic knowledge of computer science and statistics, data analysis, fundamental AI/ML concepts, Python and R.

Exam Format

50 questions, 70% passing, 90 minutes, online proctored exam

Course Modules

1

Module 1: Introduction to Robotics and Artificial Intelligence (AI)

  • 1.1 Overview of Robotics: Introduction, History, Evolution, and Impact
  • 1.2 Introduction to Artificial Intelligence (AI) in Robotics
  • 1.3 Fundamentals of Machine Learning (ML) and Deep Learning
  • 1.4 Role of Neural Networks in Robotics
2

Module 2: Understanding AI and Robotics Mechanics

  • 2.1 Components of AI Systems and Robotics
  • 2.2 Deep Dive into Sensors, Actuators, and Control Systems
  • 2.3 Exploring Machine Learning Algorithms in Robotics
3

Module 3: Autonomous Systems and Intelligent Agents

  • 3.1 Introduction to Autonomous Systems
  • 3.2 Building Blocks of Intelligent Agents
  • 3.3 Case Studies: Autonomous Vehicles and Industrial Robots
  • 3.4 Key Platforms for Development: ROS (Robot Operating System)
4

Module 4: AI and Robotics Development Frameworks

  • 4.1 Python for Robotics and Machine Learning
  • 4.2 TensorFlow and PyTorch for AI in Robotics
  • 4.3 Introduction to Other Essential Frameworks
5

Module 5: Deep Learning Algorithms in Robotics

  • 5.1 Understanding Deep Learning: Neural Networks, CNNs
  • 5.2 Robotic Vision Systems: Object Detection, Recognition
  • 5.3 Hands-on Session: Training a CNN for Object Recognition
  • 5.4 Use-case: Precision Manufacturing with Robotic Vision
6

Module 6: Reinforcement Learning in Robotics

  • 6.1 Basics of Reinforcement Learning (RL)
  • 6.2 Implementing RL Algorithms for Robotics
  • 6.3 Hands-on Session: Developing RL Models for Robots
  • 6.4 Use-case: Optimizing Warehouse Operations with RL
7

Module 7: Generative AI for Robotic Creativity

  • 7.1 Exploring Generative AI: GANs and Applications
  • 7.2 Creative Robots: Design, Creation, and Innovation
  • 7.3 Hands-on Session: Generating Novel Designs for Robotics
  • 7.4 Use-case: Custom Manufacturing with AI
8

Module 8: Natural Language Processing (NLP) for Human-Robot Interaction

  • 8.1 Introduction to NLP for Robotics
  • 8.2 Voice-Activated Control Systems
  • 8.3 Hands-on Session: Creating a Voice-command Robot Interface
  • 8.4 Case-Study: Assistive Robots in Healthcare
9

Module 9: Practical Activities and Use-Cases

  • 9.1 Hands-on Session-1: Building AI Models for Object Recognition using Python Programming
  • 9.2 Hands-on Session-2: Path Planning, Obstacle Avoidance, and Localization Implementation using Python Programming
  • 9.3 Hands-on Session-3: PID Controller Implementation using Python programming
  • 9.4 Use-cases: Precision Agriculture, Automated Assembly Lines
10

Module 10: Emerging Technologies and Innovation in Robotics

  • 10.1 Integration of Blockchain and Robotics
  • 10.2 Quantum Computing and Its Potential
11

Module 11: Exploring AI with Robotic Process Automation

  • 11.1 Understanding Robotic Process Automation and its use cases
  • 11.2 Popular RPA Tools and Their Features
  • 11.3 Integrating AI with RPA
12

Module 12: AI Ethics, Safety, and Policy

  • 12.1 Ethical Considerations in AI and Robotics
  • 12.2 Safety Standards for AI-Driven Robotics
  • 12.3 Discussion: Navigating AI Policies and Regulations
13

Module 13: Innovations and Future Trends in AI and Robotics

  • 13.1 Latest Innovations in Robotics and AI
  • 13.2 Future of Work and Society: Impact of AI and Robotics
14

Optional Module: AI Agents for Robotics

  1. 1. What Are AI Agents
  2. 2. Key Capabilities of AI Agents in Robotics
  3. 3. Applications and Trends for AI Agents in Robotics
  4. 4. How Does an AI Agent Work
  5. 5. Core Characteristics of AI Agents
  6. 6. The Future of AI Agents in Robotics
  7. 7. Types of AI Agents

AI Tools You'll Learn

OpenAI Gym

OpenAI Gym

GreyOrange

GreyOrange

Neurala

Neurala

Dialogflow

Dialogflow