AI for Robotics Training in Brazil

Practical Rapid Prototyping for Robotics with ROS 2 & Docker is a practical course aimed at helping developers build, test, and deploy robotic applications with efficiency. Participants will learn how to containerize robotics environments, integrate ROS 2 packages, and prototype modular robotic systems using Docker to ensure reproducibility and scalability. The course highlights agility, version control, and collaboration practices that are ideal for early-stage development and innovation teams.

This instructor-led live training (available online or onsite) targets beginner to intermediate participants who want to accelerate their 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.

Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control is a practical course aimed at introducing participants to the design and implementation of intuitive interfaces for human–robot communication. This training blends theoretical concepts, design principles, and programming practice to help participants create natural and responsive interaction systems using speech, gesture, and shared control techniques. Attendees will learn how to integrate perception modules, develop multimodal input systems, and design robots that safely collaborate with humans.

This instructor-led, live training (available online or onsite) is designed for 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: ROS-PLC Integration & Digital Twins is a practical course designed to bridge the gap between industrial automation and modern robotics frameworks. Participants will learn how to integrate ROS-based robotic systems with PLCs for synchronized operations and explore digital twin environments to simulate, monitor, and optimize production processes. The course emphasizes interoperability, real-time control, and predictive analysis using digital replicas of physical systems.

This instructor-led, live training (available online or onsite) targets intermediate-level professionals who want to develop practical skills in connecting ROS-controlled robots with PLC environments and implementing digital twins for automation and manufacturing optimization.

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

• Understand the communication protocols between ROS and PLC systems.
• Implement real-time data exchange between robots and industrial controllers.
• Develop digital twins for monitoring, testing, and process simulation.
• Integrate sensors, actuators, and robotic manipulators within industrial workflows.
• Design and validate industrial automation systems using hybrid simulation environments.

Format of the Course

• Interactive lecture and architecture walkthroughs.
• Hands-on exercises integrating ROS and PLC systems.
• Simulation and digital twin project implementation.

Course Customization Options

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

Robotic Manipulation and Grasping Using Deep Learning is an advanced program that integrates robotic control with contemporary machine learning methodologies. Participants will investigate how deep learning can improve perception, motion planning, and dexterous grasping capabilities within robotic systems. Through a blend of theoretical concepts, simulations, and practical coding activities, the course leads learners from perception-driven control to end-to-end policy learning for manipulation tasks.

This instructor-led live training (available online or onsite) targets advanced-level professionals seeking to leverage deep learning techniques to facilitate intelligent, adaptable, and precise robotic manipulation.

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

• Build perception models for object recognition and pose estimation.
• Train neural networks for grasp detection and motion planning.
• Integrate deep learning components with robotic controllers using ROS 2.
• Simulate and assess grasping and manipulation strategies in virtual environments.
• Deploy and refine learned models on real or simulated robotic arms.

Course Format

• 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.

The Multi-Robot Systems and Swarm Intelligence advanced training course delves into the design, coordination, and control of robotic teams, drawing inspiration from biological swarm behaviors. Participants will acquire the skills to model interactions, implement distributed decision-making processes, and optimize collaboration across multiple agents. By blending theoretical foundations with practical simulation exercises, the course prepares learners for real-world applications in logistics, defense, search and rescue operations, and autonomous exploration.

This instructor-led training is available either online or onsite, catering to advanced professionals who aim to design, simulate, and deploy multi-robot and swarm-based systems utilizing open-source frameworks and algorithms.

Upon completing this training, participants will be capable of:

• Grasping the core principles and dynamics of swarm intelligence and cooperative robotics.
• Formulating communication and coordination strategies tailored for multi-robot systems.
• Deploying distributed decision-making mechanisms and consensus algorithms.
• Simulating collective behaviors including formation control, flocking dynamics, and area coverage.
• Applying swarm-based methodologies to practical scenarios and complex optimization challenges.

Course Format

• In-depth lectures featuring algorithmic analysis.
• Practical coding sessions and simulations using ROS 2 and Gazebo.
• A collaborative project focused on applying swarm intelligence principles.

Customization Options for the Course

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

TinyML enables the deployment of machine learning models on low-power microcontrollers and embedded platforms, which are essential for robotics and autonomous systems.

This instructor-led live training (available online or onsite) is designed for advanced professionals looking to incorporate TinyML-based perception and decision-making into autonomous robots, drones, and intelligent control systems.

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

• Design optimized TinyML models tailored for robotics applications.
• Implement on-device perception pipelines to enable real-time autonomy.
• Integrate TinyML solutions into existing robotic control frameworks.
• Deploy and test lightweight AI models on embedded hardware platforms.

Course Format

• Technical lectures paired with interactive discussions.
• Hands-on labs centered on embedded robotics tasks.
• Practical exercises that simulate real-world autonomous workflows.

Course Customization Options

• Customization for organization-specific robotics environments can be arranged upon request.

Safe and Explainable Robotics is a comprehensive training program focused on the safety, verification, and ethical governance of robotic systems. The course connects theory with practice by exploring safety case methodologies, hazard analysis, and explainable AI approaches that make robotic decision-making transparent and trustworthy. Participants will learn how to ensure compliance, verify behaviors, and document safety assurance in accordance with international standards.

This instructor-led, live training (available online or onsite) is designed for intermediate-level professionals who wish to apply verification, validation, and explainability principles to ensure the safe and ethical deployment of robotic systems.

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

• Develop and document safety cases for robotic and autonomous systems.
• Apply verification and validation techniques in simulation environments.
• Understand explainable AI frameworks for robotics decision-making.
• Integrate safety and ethics principles into system design and operation.
• Communicate safety and transparency requirements to stakeholders.

Course Format

• Interactive lectures and discussions.
• Hands-on exercises involving simulation and safety analysis.
• Case studies drawn from real-world robotics applications.

Course Customization Options

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

Edge AI allows artificial intelligence models to execute directly on embedded or resource-limited devices, which reduces latency and power usage while enhancing autonomy and privacy within robotic systems.

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

Upon completing this training, participants will be capable of:

• Grasping the core principles of TinyML and edge AI for robotics.
• Converting and deploying AI models for on-device inference.
• Optimizing models to improve speed, reduce size, and increase energy efficiency.
• Integrating edge AI systems into robotic control architectures.
• Evaluating performance and accuracy in practical, real-world scenarios.

Course Format

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

Course Customization Options

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

This live, instructor-led training in Brazil (offered online or onsite) is designed for intermediate-level learners interested in exploring the influence of collaborative robots (cobots) and other human-centric AI solutions in modern workspaces.

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

• Comprehend the foundational concepts of Human-Centric Physical AI and their real-world uses.
• Evaluate the role of collaborative robots in driving workplace productivity.
• Identify and resolve challenges related to human-machine interaction.
• Develop workflows that enhance cooperation between humans and AI systems.
• Cultivate a culture of innovation and adaptability in workplaces adopting AI technologies.

Reinforcement learning (RL) constitutes a machine learning paradigm wherein agents acquire decision-making skills by engaging with an environment. Within the field of robotics, RL empowers autonomous systems to cultivate adaptive control and decision-making competencies through experience and feedback.

This instructor-led training session, available online or onsite, is designed for advanced machine learning engineers, robotics researchers, and developers who aim to design, implement, and deploy reinforcement learning algorithms within robotic applications.

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

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

Course Format

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

OpenCV is an open-source computer vision library that enables real-time image processing, while deep learning frameworks such as TensorFlow provide the tools for intelligent perception and decision-making in robotic systems.

This instructor-led, live training (online or onsite) is aimed at intermediate-level robotics engineers, computer vision practitioners, and machine learning engineers who wish to apply computer vision and deep learning techniques for robotic perception and autonomy.

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

• Build computer vision pipelines using OpenCV.
• Incorporate deep learning models for object detection and recognition.
• Leverage vision-based data for robotic control and navigation.
• Blend classical vision algorithms with deep neural networks.
• Deploy computer vision systems on embedded and robotic platforms.

Format of the Course

• Interactive lecture and discussion.
• Hands-on practice using OpenCV and TensorFlow.
• Live-lab implementation on simulated or physical robotic systems.

Course Customization Options

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

This instructor-led, live training in Brazil (online or onsite) targets advanced robotics engineers and AI researchers aiming to leverage Multimodal AI to integrate diverse sensory data, thereby creating more autonomous and efficient robots with capabilities in vision, hearing, and touch.

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.
• Build robots capable of executing complex tasks in dynamic environments.
• Overcome challenges related to real-time data processing and actuation.

Intelligent robotics involves embedding artificial intelligence into robotic systems to enhance perception, decision-making capabilities, and autonomous control.

This instructor-led training (available online or on-site) is designed for advanced robotics engineers, systems integrators, and automation leads who want to implement AI-driven perception, planning, and control within smart manufacturing environments.

Upon completing this training, participants will be able to:

• Comprehend and apply AI techniques for robotic perception and sensor fusion.
• Create motion planning algorithms for both collaborative and industrial robots.
• Implement learning-based control strategies to enable real-time decision-making.
• Integrate intelligent robotic systems into smart factory workflows.

Course Format

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

Customization Options

• For a customized training session, please contact us to make arrangements.

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 (available online or onsite) targets intermediate-level robotics engineers and developers aiming to implement autonomous navigation and SLAM (Simultaneous Localization and Mapping) using ROS 2.

Upon completing 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 lectures and discussions.
• 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 customized training for this course, please contact us to arrange.

This instructor-led, live training in Brazil (online or onsite) is aimed at intermediate-level participants who wish to enhance their skills in designing, programming, and deploying intelligent robotic systems for automation and beyond.

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

• Understand the principles of Physical AI and its applications in robotics and automation.
• Design and program intelligent robotic systems for dynamic environments.
• Implement AI models for autonomous decision-making in robots.
• Leverage simulation tools for robotic testing and optimization.
• Address challenges such as sensor fusion, real-time processing, and energy efficiency.

Artificial Intelligence (AI) for Robotics merges machine learning, control systems, and sensor fusion to build intelligent machines that can perceive, reason, and act independently. By leveraging modern tools such as ROS 2, TensorFlow, and OpenCV, engineers are now able to design robots that navigate, plan, and interact with real-world environments intelligently.

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

Upon completing this training, participants will be able to:

• Utilize Python and ROS 2 to create 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.
• Use TensorFlow for motion prediction and learning-based control strategies.
• Integrate SLAM (Simultaneous Localization and Mapping) to enable autonomous navigation.
• Develop reinforcement learning models to enhance robotic decision-making capabilities.

Format of the Course

• Interactive lectures and discussions.
• Practical implementation using ROS 2 and Python.
• Hands-on exercises within simulated and real robotic environments.

Course Customization Options

To request a customized training session for this course, please get in touch with us to arrange your preferences.

A bot or chatbot functions as a digital assistant designed to automate user interactions across various messaging platforms, enabling faster task completion without requiring direct contact with a human agent.

In this instructor-led live training, participants will learn how to begin developing bots by creating sample chatbots using established bot development tools and frameworks.

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

• Identify the various uses and applications of bots
• Grasp the complete process involved in bot development
• Explore the diverse tools and platforms utilized for building bots
• Construct a sample chatbot for Facebook Messenger
• Build a sample chatbot using the Microsoft Bot Framework

Audience

• Developers interested in creating their own bots

Format of the course

• Part lecture, part discussion, exercises, and extensive hands-on practice

This instructor-led live training in Brazil (online or onsite) is designed for engineers who wish to learn about 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.