Physical AI for Robotics and Automation Training Course

Artificial Intelligence (AI) for Robotics merges machine learning, control systems, and sensor fusion to build intelligent machines capable of perceiving, reasoning, and acting autonomously. Leveraging modern tools such as ROS 2, TensorFlow, and OpenCV, engineers can design robots that navigate, plan, and interact with real-world environments with sophistication.

This instructor-led, live training (available online or onsite) is tailored for intermediate-level engineers looking to develop, train, and deploy AI-driven robotic systems using contemporary open-source technologies and frameworks.

Upon completion of this training, participants will be able to:

• Utilize Python and ROS 2 to construct and simulate robotic behaviors.
• Implement Kalman and Particle Filters for localization and tracking purposes.
• Apply computer vision techniques via OpenCV for perception and object detection.
• Employ TensorFlow for motion prediction and learning-based control.
• Integrate SLAM (Simultaneous Localization and Mapping) to enable autonomous navigation.
• Develop reinforcement learning models to enhance robotic decision-making.

Format of the Course

• Interactive lecture and discussion.
• Hands-on implementation using ROS 2 and Python.
• Practical exercises involving simulated and real robotic environments.

Course Customization Options

To request a customized training for this course, please contact us to arrange.

ROS 2 (Robot Operating System 2) is an open-source framework designed to support the development of complex and scalable robotic applications.

This instructor-led, live training (online or onsite) is aimed at intermediate-level robotics engineers and developers who wish to implement autonomous navigation and SLAM (Simultaneous Localization and Mapping) using ROS 2.

By the end of this training, participants will be able to:

• Set up and configure ROS 2 for autonomous navigation applications.
• Implement SLAM algorithms for mapping and localization.
• Integrate sensors such as LiDAR and cameras with ROS 2.
• Simulate and test autonomous navigation in Gazebo.
• Deploy navigation stacks on physical robots.

Format of the Course

• Interactive lecture and discussion.
• Hands-on practice using ROS 2 tools and simulation environments.
• Live-lab implementation and testing on virtual or physical robots.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

OpenCV serves as an open-source computer vision library that facilitates real-time image processing, while deep learning frameworks like TensorFlow equip robotic systems with the capabilities needed for intelligent perception and decision-making.

This instructor-led live training, available online or onsite, is designed for intermediate-level robotics engineers, computer vision specialists, and machine learning professionals who want to leverage computer vision and deep learning techniques to enhance robotic perception and autonomy.

Upon completing this training, participants will be able to:

• Build computer vision pipelines using OpenCV.
• Integrate deep learning models for the detection and recognition of objects.
• Leverage vision-based data for controlling and navigating robots.
• Blend classical vision algorithms with deep neural networks.
• Deploy computer vision solutions on embedded and robotic platforms.

Course Format

• Interactive lectures and discussions.
• Practical exercises using OpenCV and TensorFlow.
• Live laboratory implementation on simulated or physical robotic systems.

Customization Options

• To arrange a customized training session for this course, please reach out to us.

A bot, or chatbot, acts as a virtual assistant designed to automate user interactions across various messaging platforms, enabling tasks to be completed more efficiently without requiring direct human intervention.

In this instructor-led live training, participants will learn the fundamentals of bot development by creating sample chatbots using dedicated development tools and frameworks.

By the conclusion of this training, participants will be able to:

• Grasp the diverse use cases and applications of bots
• Comprehend the end-to-end bot development lifecycle
• Investigate the various tools and platforms utilized in bot construction
• Construct a sample chatbot for Facebook Messenger
• Develop a sample chatbot using the Microsoft Bot Framework

Audience

• Developers interested in creating their own bots

Course Format

• A blend of lectures, discussions, exercises, and extensive hands-on practice

Edge AI allows artificial intelligence models to operate directly on embedded or resource-constrained devices, thereby lowering latency and power usage while boosting autonomy and privacy within robotic systems.

This instructor-led live training, available online or on-site, targets intermediate-level embedded developers and robotics engineers looking to implement machine learning inference and optimization techniques directly on robotic hardware via TinyML and edge AI frameworks.

Upon completing this training, participants will be able to:

• Grasp the core principles of TinyML and edge AI for robotics.
• Convert and deploy AI models for on-device inference.
• Optimize models to improve speed, reduce size, and enhance energy efficiency.
• Integrate edge AI systems into robotic control architectures.
• Assess performance and accuracy in real-world applications.

Course Format

• Interactive lectures and discussions.
• Practical exercises utilizing TinyML and edge AI toolchains.
• Hands-on work with embedded and robotic hardware platforms.

Customization Options

• To arrange a tailored training session for this course, please contact us.

This instructor-led, live training in Italy (online or onsite) is aimed at intermediate-level participants who wish to explore the role of collaborative robots (cobots) and other human-centric AI systems in modern workplaces.

By the end of this training, participants will be able to:

• Understand the principles of Human-Centric Physical AI and its applications.
• Explore the role of collaborative robots in enhancing workplace productivity.
• Identify and address challenges in human-machine interactions.
• Design workflows that optimize collaboration between humans and AI-driven systems.
• Promote a culture of innovation and adaptability in AI-integrated workplaces.

Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control is a practical course designed to introduce participants to the design and implementation of intuitive interfaces for human–robot communication. The training combines theory, design principles, and programming practice to build natural and responsive interaction systems using speech, gesture, and shared control techniques. Participants will learn how to integrate perception modules, develop multimodal input systems, and design robots that safely collaborate with humans.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level participants who wish to design and implement human–robot interaction systems that enhance usability, safety, and user experience.

By the end of this training, participants will be able to:

• Understand the foundations and design principles of human–robot interaction.
• Develop voice-based control and response mechanisms for robots.
• Implement gesture recognition using computer vision techniques.
• Design collaborative control systems for safe and shared autonomy.
• Evaluate HRI systems based on usability, safety, and human factors.

Format of the Course

• Interactive lectures and demonstrations.
• Hands-on coding and design exercises.
• Practical experiments in simulation or real robotic environments.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Industrial Robotics Automation: Integrating ROS and PLC with Digital Twins is a practical course designed to bridge the gap between traditional industrial automation and modern robotics frameworks. Participants will gain expertise in synchronizing ROS-based robotic systems with PLCs and utilize digital twin environments to simulate, monitor, and optimize manufacturing processes. The curriculum places a strong emphasis on system interoperability, real-time control mechanisms, and predictive analytics enabled by digital replicas of physical assets.

This instructor-led training, available in both online and onsite formats, targets intermediate-level professionals seeking to develop practical competencies in connecting ROS-controlled robots with PLC ecosystems and deploying digital twins to enhance automation and manufacturing efficiency.

Upon completion of this training, participants will be capable of:

• Analyzing communication protocols that facilitate interaction between ROS and PLC systems.
• Executing real-time data exchange mechanisms between robots and industrial controllers.
• Creating digital twins for monitoring, testing, and simulating operational processes.
• Integrating sensors, actuators, and robotic manipulators into industrial workflows.
• Designing and validating industrial automation solutions within hybrid simulation environments.

Course Format

• Interactive lectures accompanied by detailed architecture walkthroughs.
• Practical exercises focused on integrating ROS and PLC systems.
• Implementation of simulation and digital twin projects.

Customization Options

• For organizations requiring tailored training on this subject, please reach out to us to arrange a customized session.

This instructor-led live training in Italy (online or onsite) is designed for engineers who wish to explore the applicability of artificial intelligence to mechatronic systems.

By the end of this training, participants will be able to:

• Gain an overview of artificial intelligence, machine learning, and computational intelligence.
• Understand the concepts of neural networks and different learning methods.
• Choose artificial intelligence approaches effectively for real-life problems.
• Implement AI applications in mechatronic engineering.

Multi-Robot Systems and Swarm Intelligence is an advanced training course that delves into the design, coordination, and control of robotic teams inspired by biological swarm behaviors. Participants will learn how to model interactions, implement distributed decision-making, and optimize collaboration across multiple agents. The course combines theory with hands-on simulation to prepare learners for applications in logistics, defense, search and rescue, and autonomous exploration.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to design, simulate, and implement multi-robot and swarm-based systems using open-source frameworks and algorithms.

By the end of this training, participants will be able to:

• Understand the principles and dynamics of swarm intelligence and cooperative robotics.
• Design communication and coordination strategies for multi-robot systems.
• Implement distributed decision-making and consensus algorithms.
• Simulate collective behaviors such as formation control, flocking, and coverage.
• Apply swarm-based techniques to real-world scenarios and optimization problems.

Format of the Course

• Advanced lectures with algorithmic deep dives.
• Hands-on coding and simulation in ROS 2 and Gazebo.
• Collaborative project applying swarm intelligence principles.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Italy (online or onsite) targets advanced robotics engineers and AI researchers seeking to leverage Multimodal AI to integrate diverse sensory data, thereby creating more autonomous and efficient robots capable of seeing, hearing, and touching.

Upon completion of this training, participants will be able to:

• Implement multimodal sensing within robotic systems.
• Develop AI algorithms for sensor fusion and decision-making processes.
• Construct robots capable of executing complex tasks in dynamic environments.
• Tackle challenges related to real-time data processing and actuation.

Practical Rapid Prototyping for Robotics with ROS 2 & Docker is a hands-on course designed to help developers build, test, and deploy robotic applications efficiently. Participants will learn how to containerize robotics environments, integrate ROS 2 packages, and prototype modular robotic systems using Docker for reproducibility and scalability. The course emphasizes agility, version control, and collaboration practices suitable for early-stage development and innovation teams.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level participants who wish to accelerate robotics development workflows using ROS 2 and Docker.

By the end of this training, participants will be able to:

• Set up a ROS 2 development environment within Docker containers.
• Develop and test robotic prototypes in modular, reproducible setups.
• Use simulation tools to validate system behavior before hardware deployment.
• Collaborate effectively using containerized robotics projects.
• Apply continuous integration and deployment concepts in robotics pipelines.

Format of the Course

• Interactive lectures and demonstrations.
• Hands-on exercises with ROS 2 and Docker environments.
• Mini-projects focused on real-world robotic applications.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Reinforcement learning (RL) constitutes a machine learning approach where agents acquire decision-making skills by interacting with their surrounding environment. Within the field of robotics, RL empowers autonomous systems to cultivate adaptive control and decision-making abilities driven by experience and feedback.

This instructor-led, live training (available online or onsite) is designed for advanced-level machine learning engineers, robotics researchers, and developers interested in designing, implementing, and deploying reinforcement learning algorithms for robotic applications.

Upon completion of this training, participants will be capable of:

• Grasping the core principles and mathematical foundations of reinforcement learning.
• Implementing RL algorithms including Q-learning, DDPG, and PPO.
• Integrating RL into robotic simulation environments using OpenAI Gym and ROS 2.
• Training robots to execute complex tasks autonomously through trial and error.
• Enhancing training performance utilizing deep learning frameworks such as PyTorch.

Format of the Course

• Interactive lectures and discussions.
• Practical implementation using Python, PyTorch, and OpenAI Gym.
• Hands-on exercises within simulated or physical robotic environments.

Course Customization Options

• To arrange a customized training session for this course, please get in touch with us.

The course on Robot Manipulation and Grasping with Deep Learning represents an advanced program that connects robotic control with contemporary machine learning methodologies. Participants will investigate how deep learning can improve perception, motion planning, and dexterous grasping within robotic systems. Through theoretical study, simulation, and practical coding sessions, the course leads learners from perception-based control to end-to-end policy learning for manipulation tasks.

This instructor-led, live training (available online or onsite) is designed for advanced professionals seeking to apply deep learning methods to achieve intelligent, adaptable, and precise robotic manipulation.

Upon completion of this training, participants will be capable of:

• Creating perception models for object recognition and pose estimation.
• Training neural networks for grasp detection and motion planning.
• Combining deep learning modules with robotic controllers via ROS 2.
• Simulating and evaluating grasping and manipulation strategies in virtual environments.
• Deploying and optimizing learned models on real or simulated robotic arms.

Format of the Course

• Expert-led lectures and algorithmic deep dives.
• Hands-on coding and simulation exercises.
• Project-based implementation and testing.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Smart Robotics involves integrating artificial intelligence into robotic systems to enhance perception, decision-making capabilities, and autonomous control.

This instructor-led live training, available either online or on-site, is designed for advanced robotics engineers, systems integrators, and automation leaders who aim to implement AI-driven perception, planning, and control within smart manufacturing settings.

Upon completing this training, participants will be capable of:

• Understanding and applying AI techniques for robotic perception and sensor fusion.
• Developing motion planning algorithms for both collaborative and industrial robots.
• Deploying learning-based control strategies to facilitate real-time decision making.
• Integrating intelligent robotic systems into smart factory workflows.

Course Format

• Interactive lectures and discussions.
• Numerous exercises and practical practice sessions.
• Hands-on implementation within a live-lab environment.

Course Customization Options

• To request customized training for this course, please reach out to us to arrange the details.