The AI+ Robotics™ certification program offers a transformative journey into the dynamic intersection of Artificial Intelligence (AI) and Robotics. From foundational concepts to advanced Deep Learning algorithms and Reinforcement Learning, the immersive experience is tailored for Robotics applications. Each module provides a well-rounded understanding, exploring autonomous systems, intelligent agents, and generative AI. Through hands-on activities and real world case studies, practical skills are honed.

Ethical considerations and policy frameworks are navigated responsibly. Stay updated on emerging trends, shaping the future of the industry. By the program's end, acquire both robust theoretical knowledge and practical expertise, empowering you to lead innovation in the ever-evolving AI and Robotics landscape.

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

Exam Details

• Modules – 13
• Examination – 1
• 50 MCQs - 90 Minutes
• Passing Score – 70%

Certification Modules

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

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

Module 2: Understanding AI and Robotics Mechanics

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

Module 3: Autonomous Systems and Intelligent Agents

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

Module 4: AI and Robotics Development Frameworks

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

Module 5: Deep Learning Algorithms in Robotics

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

Module 6: Reinforcement Learning in Robotics

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

Module 7: Generative AI for Robotic Creativity

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

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

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

Module 9: Practical Activities and Use-Cases

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

Module 10: Emerging Technologies and Innovation in Robotics

• Integration of Blockchain and Robotics
• Quantum Computing and Its Potential

Module 11: Exploring AI with Robotic Process Automation

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

Module 12: AI Ethics, Safety, and Policy

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

Module 13: Innovations and Future Trends in AI and Robotics

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

 

Tools

• OpenAI Gym
• GreyOrange
• Neurala
• Dialogflow

 

Exam Objectives

• 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.