AI+ Robotics PractitionerTM

Covers AI-driven robotics, autonomous systems, and intelligent agents, with ethics and hands-on projects for designing and deploying robotics solutions.

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Design and deploy AI-driven robotics

  • 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:
    Design, optimize, and deploy robotics solutions

Prerequisites

  • Basic computer science and statistics
  • Fundamental AI/ML concepts
  • Python and R (helpful)
  • Ability to analyze information critically

Why This Certification Matters?

  • Demand for certified professionals Organizations seek professionals who can integrate AI into robotics to optimize processes and efficiency.
  • Risks of mismanagement Mismanaged robotic systems and AI can cause operational inefficiencies and safety risks.
  • Role in robotics strategy Certified professionals help build strategies that maximize performance, safety, and compliance.
  • Career advantage Positions professionals for leadership roles as robotics and AI reshape industries.

What You'll Learn?

Overview of Robotics: history, evolution, and impact
Introduction to AI in robotics
Fundamentals of machine learning and deep learning
Role of neural networks in robotics

Components of AI systems and robotics
Sensors, actuators, and control systems
Machine learning algorithms in robotics

Introduction to autonomous systems
Building blocks of intelligent agents
Case studies: autonomous vehicles and industrial robots
Key development platforms: ROS

Python for robotics and machine learning
TensorFlow and PyTorch for AI in robotics
Other essential frameworks

Neural networks and CNNs
Robotic vision: object detection and recognition
Hands-on: training a CNN for object recognition
Use case: precision manufacturing with robotic vision

Basics of reinforcement learning
Implementing RL algorithms for robotics
Hands-on: developing RL models for robots
Use case: optimizing warehouse operations

GANs and applications
Creative robots: design and innovation
Hands-on: generating novel robotics designs
Use case: custom manufacturing with AI

Introduction to NLP for robotics
Voice-activated control systems
Hands-on: voice-command robot interface
Case study: assistive robots in healthcare

Hands-on: AI models for object recognition in Python
Hands-on: path planning, obstacle avoidance, and localization
Hands-on: PID controller implementation
Use cases: precision agriculture, automated assembly lines

Integration of blockchain and robotics
Quantum computing and its potential

RPA and its use cases
Popular RPA tools and their features
Integrating AI with RPA

Ethical considerations in AI and robotics
Safety standards for AI-driven robotics
Navigating AI policies and regulations

Latest innovations in robotics and AI
Future of work and society

What AI agents are
Key capabilities of AI agents in robotics
Applications and trends
How AI agents work
Core characteristics of AI agents
The future of AI agents in robotics
Types of AI agents

Job Roles & Industry Outlook

AI Robotics Integration Expert

Integrates AI technologies into existing robotic systems to enhance performance and enable new functions.

AI Robotics System Developer

Creates robotic systems with AI, focusing on perception, learning, and adaptive behavior.

Robotics Engineer with AI Expertise

Designs robots that use AI algorithms to enhance autonomy and decision-making.

AI Intelligent Robotics Specialist

Develops intelligent robots for navigation, manipulation, and human interaction.

Skills You’ll Gain

Robotic Process Automation (RPA)AI-Powered Control SystemsMachine Learning for RoboticsRobotic Path PlanningSensor Integration and Data Processing

Tools You’ll Master

Exam Details

Duration

90 minutes

Passing Score

70% (35/50)

Format

50 multiple-choice/multiple-response questions

Delivery Method

Online via proctored exam platform (flexible scheduling)

Exam Blueprint

Introduction to Robotics and AI
5%
AI and Robotics Mechanics
6%
Autonomous Systems and Intelligent Agents
6%
Development Frameworks
9%
Deep Learning Algorithms in Robotics
9%
Reinforcement Learning in Robotics
9%
Generative AI for Robotic Creativity
9%
NLP for Human-Robot Interaction
9%
Practical Activities and Use-Cases
8%
Emerging Technologies
9%
AI with RPA
9%
AI Ethics, Safety, and Policy
6%
Innovations and Future Trends
6%

What’s Included

(One-Year Subscription + All Updates)

  • High-Quality Videos, E-book (PDF & Audio), and Podcasts 
  • AI Mentor for Personalized Guidance 
  • Quizzes, Assessments, and Course Resources 
  • Online Proctored Exam with One Free Retake 
  • Comprehensive Exam Study Guide 
  • Access for Tablet & Phone