Opleiding: AI+ Robotics Practitioner™

Formerly known as AI+ Robotics™

Build the Future with Smart Automation

• 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

Robotics Engineers Enhance robotic system design and functionality using AI for automation and control.

Mechanical Engineers: Integrate AI to optimize robotics systems and improve performance in manufacturing and production.

AI Specialists: Apply AI techniques to enhance the intelligence and autonomy of robotic systems.

IT Specialists & System Integrators: Implement AI-powered solutions to improve robotics infrastructure and communication systems.

Students & New Graduates: Build essential skills in AI and robotics to succeed in an emerging field with endless growth potential.

• Demand for Certified AI & Robotics Professionals:Organizations are seeking certified professionals who can integrate AI into robotics to optimize processes, enhance automation, and improve operational efficiency.
• Risks of Mismanaging AI & Robotics:Mismanagement of robotic systems and AI technologies can lead to operational inefficiencies and safety risks.
• Role of Certification in Robotics Strategy:Certified professionals are key in developing robotics strategies that maximize performance, safety, and compliance with industry regulations.
• Career Advantage & Leadership Opportunities:As robotics and AI continue to reshape industries, this certification offers professionals a distinct advantage, positioning them for leadership roles.

• 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

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

• 3.1 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)

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

• 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

• 6.1 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

• 7.1 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

• 8.1 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

• 9.1 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

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

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

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

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

• OpenAI Gym
• GreyOrange
• Neurala
• Dialogflow

Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam

Access to all materials and exams is provided for 365 days after delivery.

De OCICT maatwerk-methode houdt in dat op basis van de behoefte van de klant - en indien nodig na een intakegesprek - een individueel opleidingstraject wordt opgesteld. De trainingen worden veelal individueel gevolgd, zodat iedere training een maatwerktraject is. Klassikale trainingen zijn natuurlijk mogelijk, mits ze aansluiten bij de leerbehoeften van de deelnemers / opdrachtgever.

Alle onderdelen van de training kennen een voortdurende wisselwerking tussen theorie en praktijk. De inhoud van de training kan vooraf per cursist worden aangepast en tijdens de cursus worden bijgesteld. De focus ligt op maatwerk: het traject wordt afgestemd op de doelstellingen van de deelnemer(s) en/of opdrachtgever, ongeacht of het individueel of klassikaal wordt aangeboden.