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Quest Training

AI-Powered Robotics & Intelligent Systems Training Course

Examines how machine learning, computer vision, sensing and control combine to give robots stronger perception, reasoning and autonomous decision-making.

AIRP · AI Robotics & Intelligent SystemsAll Levels#2115ClassroomEnglish , Arabic
Duration
5 Days
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy

Sectors:Artificial Intelligence & ApplicationsCategories:AI Robotics & Intelligent Systems

Course Overview

This advanced training course provides a comprehensive understanding of AI-powered robotics and intelligent systems, focusing on how Artificial Intelligence, Machine Learning, Computer Vision, sensing, control, and autonomous decision-making can be integrated to create sophisticated robotic solutions. The program explores the architecture of intelligent robotic systems, including perception, sensor fusion, environmental understanding, motion planning, decision-making, autonomous navigation, task execution, human-robot interaction, and adaptive learning. Participants will examine how AI enables robots to operate more effectively in dynamic and complex environments. Particular emphasis is placed on developing intelligent robotic solutions for industrial and enterprise applications. The course covers autonomous systems, intelligent inspection, predictive maintenance, collaborative robotics, automated material handling, smart manufacturing, energy operations, logistics, and other applications where AI-enabled robotics can improve productivity, safety, accuracy, and operational performance. Through practical exercises, system-design workshops, case studies, and an integrated final project, participants will develop the ability to assess, design, integrate, and evaluate AI-powered robotic systems while considering performance, safety, cybersecurity, governance, scalability, and operational requirements.

Learning Objectives

  • By the end of the course, participants will be able to:
  • Understand the architecture and operating principles of AI-powered robotic systems.
  • Analyze how AI technologies enhance robotic perception, reasoning, and decision-making.
  • Apply Machine Learning and Deep Learning concepts to robotic applications.
  • Design intelligent perception and sensor-fusion mechanisms.
  • Apply Computer Vision techniques to robotic inspection and environmental understanding.
  • Develop concepts for autonomous navigation, motion planning, and task execution.
  • Integrate AI models with robotic control and automation systems.
  • Design intelligent robotic workflows for industrial and enterprise applications.
  • Apply adaptive learning and feedback mechanisms to improve robotic performance.
  • Evaluate safety, cybersecurity, reliability, and governance requirements.
  • Assess the performance and scalability of AI-powered robotic solutions.
  • Develop an implementation strategy for deploying intelligent robotics within an organization.

Who Should Attend

This course is designed for Robotics Engineers, AI Engineers, Machine Learning Engineers, Automation Engineers, Software Engineers, Control Engineers, Electrical and Mechanical Engineers, Systems Engineers, and technical professionals involved in intelligent automation and robotics. It is also suitable for Operations Managers, Digital Transformation Managers, Innovation Leaders, Engineering Managers, IT Managers, Project Managers, Industrial Automation Specialists, and professionals responsible for implementing AI-enabled technologies and smart operational solutions. The program is particularly relevant to organizations seeking to integrate AI with robotic systems for manufacturing, inspection, maintenance, logistics, energy, oil and gas, warehousing, and other complex operational environments.

Learning Outcomes

  • Upon completion of the course, participants will be able to:
  • Assess the architecture and capabilities of AI-powered robotic systems.
  • Identify appropriate AI technologies for specific robotic applications.
  • Design intelligent perception systems using sensors and Computer Vision.
  • Apply sensor-fusion concepts to improve environmental awareness.
  • Develop approaches for autonomous navigation and robotic motion planning.
  • Design AI-supported decision-making and task-execution processes.
  • Integrate AI models with robotic controllers, software, and automation platforms.
  • Develop intelligent robotic workflows for industrial and enterprise processes.
  • Apply feedback and adaptive learning mechanisms to improve system performance.
  • Evaluate robotic systems based on accuracy, reliability, safety, and efficiency.
  • Implement fundamental cybersecurity, governance, and risk controls for intelligent robotics.
  • Prepare an implementation roadmap for an AI-powered robotics solution.

Course Outline

Course Outline

Day 1

AI-Powered Robotics Architecture & Intelligent Systems

  • Evolution of traditional robotics toward AI-powered robotics
  • Architecture of intelligent robotic systems
  • AI components within robotic platforms
  • Machine Learning and Deep Learning for robotics
  • Intelligent perception and environmental awareness
  • Robotic sensing and data acquisition
  • AI-based reasoning and decision-making
  • Autonomous versus remotely controlled robotics
  • Intelligent robotic workflows
  • Enterprise and industrial applications
  • Practical Application: Designing the architecture of an AI-powered robotic system for a defined operational use case
Day 2

Intelligent Perception, Computer Vision & Sensor Fusion

  • Robotic perception fundamentals
  • Types of robotic sensors and their applications
  • Camera-based perception
  • Computer Vision for robotic systems
  • Object detection and recognition
  • Image and video analysis
  • Environmental mapping
  • Sensor fusion and multi-source data integration
  • Real-time environmental awareness
  • Perception challenges in dynamic environments
  • Practical Workshop: Designing an intelligent perception and sensor-fusion framework for an autonomous robot
Day 3

Autonomous Navigation, Decision-Making & Task Execution

  • Autonomous navigation principles
  • Localization and environmental mapping
  • Path planning and motion planning
  • Obstacle detection and avoidance
  • AI-based decision-making for robotics
  • Task planning and prioritization
  • Autonomous task execution
  • Reinforcement Learning concepts for robotics
  • Feedback loops and adaptive behavior
  • Human-robot collaboration
  • Practical Application: Developing an autonomous navigation and task-execution workflow for an AI-powered robot
Day 4

Intelligent Industrial Robotics, Safety & Cybersecurity

  • AI-powered robotics in smart manufacturing
  • Intelligent inspection and quality control
  • AI-enabled predictive maintenance
  • Robotics for logistics and material handling
  • Robotics applications in energy and oil and gas
  • Collaborative robots and human-robot interaction
  • Functional safety and operational risk management
  • Cybersecurity for connected robotic systems
  • Access control, data protection, and system security
  • Governance and responsible use of autonomous robotics
  • Practical Workshop: Conducting a safety, cybersecurity, and operational risk assessment for an AI-powered robotic system
Day 5

Deployment, Optimization & Intelligent Robotics Strategy

  • Deployment architecture for AI-powered robotics
  • Integrating AI models with robotic platforms
  • Edge computing and real-time AI processing
  • System performance monitoring
  • Reliability and predictive performance management
  • Optimization of AI and robotic resources
  • Scalability and fleet management
  • Testing and validation of intelligent robotic systems
  • Measuring operational and business performance
  • AI-powered robotics implementation strategy
  • Final Integrated Workshop: Developing an end-to-end AI-Powered Robotics implementation roadmap covering system architecture, perception, Computer Vision, sensor fusion, navigation, decision-making, task execution, AI integration, safety, cybersecurity, deployment, scalability, and continuous improvement.

Upcoming Dates

Selected date

Amsterdam · 28 Dec 2026 – 1 Jan 2027 · Classroom

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OnlineScheduled
27 – 31 Dec 2026€2,700€2,430until 7 Dec
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DubaiScheduled
27 – 31 Dec 2026€4,700€4,230until 7 Dec
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AmmanScheduled
27 – 31 Dec 2026€4,000€3,600until 7 Dec
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SalalahScheduled
28 Dec 2026 – 1 Jan 2027€6,300€5,670until 7 Dec
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GenevaScheduled
28 Dec 2026 – 1 Jan 2027€7,300€6,570until 7 Dec
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MunichScheduled
28 Dec 2026 – 1 Jan 2027€6,000€5,400until 7 Dec
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MadridScheduled
28 Dec 2026 – 1 Jan 2027€6,000€5,400until 7 Dec
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FrankfurtScheduled
28 Dec 2026 – 1 Jan 2027€6,200€5,580until 7 Dec
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MarrakeshScheduled
28 Dec 2026 – 1 Jan 2027€4,700€4,230until 7 Dec
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LisbonScheduled
28 Dec 2026 – 1 Jan 2027€6,000€5,400until 7 Dec
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ManamaScheduled
28 Dec 2026 – 1 Jan 2027€5,300€4,770until 7 Dec
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CairoScheduled
28 Dec 2026 – 1 Jan 2027€3,800€3,420until 7 Dec
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ZurichScheduled
28 Dec 2026 – 1 Jan 2027€5,800€5,220until 7 Dec
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RabatScheduled
28 Dec 2026 – 1 Jan 2027€4,700€4,230until 7 Dec
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MilanScheduled
28 Dec 2026 – 1 Jan 2027€7,000€6,300until 7 Dec
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MadinahScheduled
28 Dec 2026 – 1 Jan 2027€4,800€4,320until 7 Dec
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ViennaScheduled
28 Dec 2026 – 1 Jan 2027€6,400€5,760until 7 Dec
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Kuala LumpurScheduled
28 Dec 2026 – 1 Jan 2027€4,700€4,230until 7 Dec
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AmsterdamScheduled
28 Dec 2026 – 1 Jan 2027€6,000€5,400until 7 Dec
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DammamScheduled
28 Dec 2026 – 1 Jan 2027€4,800€4,320until 7 Dec
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MuscatScheduled
28 Dec 2026 – 1 Jan 2027€6,300€5,670until 7 Dec
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LondonScheduled
28 Dec 2026 – 1 Jan 2027€5,800€5,220until 7 Dec
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BarcelonaScheduled
28 Dec 2026 – 1 Jan 2027€6,000€5,400until 7 Dec
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IstanbulScheduled
28 Dec 2026 – 1 Jan 2027€5,300€4,770until 7 Dec
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RomeScheduled
28 Dec 2026 – 1 Jan 2027€7,000€6,300until 7 Dec
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BangkokScheduled
28 Dec 2026 – 1 Jan 2027€6,700€6,030until 7 Dec
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MakkahScheduled
28 Dec 2026 – 1 Jan 2027€4,800€4,320until 7 Dec
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DohaScheduled
28 Dec 2026 – 1 Jan 2027€4,700€4,230until 7 Dec
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CasablancaScheduled
28 Dec 2026 – 1 Jan 2027€4,700€4,230until 7 Dec
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ParisScheduled
28 Dec 2026 – 1 Jan 2027€6,700€6,030until 7 Dec
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