AI+ Robotics™

Build the Future with Smart Automation

Certificate Code: AT-420

Duration: 40 hours (5 Days)

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Course Metrics Prerequisites Curriculum Outcomes Opportunities Salary Comparison FAQs Enroll

14+

Modules

40 hours (5 Days)

Duration

50 MCQs, 90 Minutes

Examination

70%

Passing Score

About This Certification

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

Prerequisites

Familiarity with basic concepts of Artificial Intelligence (AI), without the need for technical expertise.
Openness to generate innovative ideas and concepts, leveraging AI tools effectively in the process.
Ability to analyze information critically and evaluate the implications of AI and Robotics technologies.
Readiness to engage in problem-solving activities and apply AI techniques to real-world scenario

Tools Covered

OpenAI Gym

GreyOrange

Neurala

Dialogflow

Course Curriculum

14 Modules 40 hours (5 Days)

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

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

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)

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

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

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

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

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

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

Module 10: Emerging Technologies and Innovation in Robotics

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

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

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

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

Optional Module: AI Agents for Robotics

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

Exam Blueprint

Introduction to Robotics and Artificial Intelligence (AI) – 5%

Understanding AI and Robotics Mechanics – 6%

Autonomous Systems and Intelligent Agents – 6%

AI and Robotics Development Frameworks – 9%

Deep Learning Algorithms in Robotics – 9%

Reinforcement Learning in Robotics – 9%

Generative AI for Robotic Creativity – 9%

Natural Language Processing (NLP) for Human-Robot Interaction – 9%

Practical Activities and Use-Cases – 8%

Emerging Technologies and Innovation in Robotics – 9%

Exploring AI with Robotic Process Automation (RPA) – 9%

AI Ethics, Safety, and Policy – 6%

Innovations and Future Trends in AI and Robotics – 6%

Learning Outcomes

Algorithm Development and Implementation

Developing the ability to implement deep learning and reinforcement learning algorithms specifically tailored for robotics, equipping learners with the skills to create intelligent and adaptive robotic behaviors.

Human-Robot Interaction and Communication

Gaining expertise in Natural Language Processing (NLP) for facilitating effective human-robot interaction, enhancing the ability of robots to understand and respond to human commands and communications.

Generative AI for Creative Applications

Learning to apply generative AI techniques for enhancing robotic creativity, allowing robots to generate novel solutions and approaches in various tasks and problem-solving scenarios.

Practical Application and Use-Case Implementation

Developing hands-on experience through practical activities and real-world use-cases, which reinforces theoretical knowledge and provides learners with the skills to apply their learning to actual robotic projects and challenges.

Career Opportunities

AI Robotics Integration Expert:

Integrates AI technologies into existing robotic systems, enhancing their performance and enabling new functionalities and applications.

AI Robotics System Developer:

Creates complex robotic systems incorporating AI, focusing on enhancing capabilities like perception, learning, and adaptive behavior.

Robotics Engineer with AI Expertise:

Designs and develops advanced robots, integrating AI algorithms to enhance autonomy, decision-making, and overall robotic functionality.

AI Intelligent Robotics Specialist:

Specializes in developing intelligent robots that utilize AI for advanced tasks, such as navigation, manipulation, and human interaction.

Salary Potential

Without AI Skills

$59,391

With AI Skills

$130,558

120% Higher Earning Potential

Frequently Asked Questions

What is the AI+ Robotics™ Certification about?

The AI+ Robotics™ Certification provides a comprehensive understanding of the intersection of Artificial Intelligence (AI) and Robotics.

Who should enroll in this certification?

This certification is ideal for professionals and enthusiasts interested in AI and Robotics, including those with basic familiarity with AI concepts.

What practical skills will I gain from this certification?

You will gain hands-on experience in building AI models, training neural networks, developing reinforcement learning models.

How will this certification benefit my career?

This certification will enhance your skills in AI and Robotics, making you a valuable asset in industries adopting automation and AI-driven solutions.

What are the prerequisites for enrolling in the certification?

Participants should have a basic understanding of AI concepts, be open to generating innovative ideas, have the ability to critically analyze information.

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