Online AI for Robotics Training in Argentina

This practical course on Rapid Prototyping in Robotics with ROS 2 and Docker provides developers with the skills to efficiently build, test, and deploy robotic applications. Learners will acquire knowledge on containerizing robotic environments, integrating ROS 2 packages, and prototyping modular robotic systems using Docker to ensure reproducibility and scalability. The curriculum focuses on agility, version control, and collaboration techniques tailored for early-stage development and innovation teams.

This instructor-led live training, available online or onsite, targets beginner to intermediate participants seeking to speed up their robotics development workflows through ROS 2 and Docker.

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

• Establishing a ROS 2 development environment within Docker containers.
• Developing and testing robotic prototypes in modular, reproducible configurations.
• Utilizing simulation tools to verify system behavior prior to hardware deployment.
• Collaborating effectively through containerized robotics projects.
• Applying continuous integration and deployment principles within robotics pipelines.

Course Structure

• Interactive lectures and live demonstrations.
• Practical exercises involving ROS 2 and Docker environments.
• Mini-projects centered on real-world robotic applications.

Customization Options

• For requests regarding customized training for this course, please contact us to arrange it.

The course 'Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control' offers a practical approach to mastering the design and implementation of intuitive interfaces for human–robot communication. This training blends theoretical knowledge, design principles, and programming practice to help participants create natural and responsive interaction systems utilizing speech, gesture, and shared control techniques. Attendees will gain expertise in integrating perception modules, developing multimodal input systems, and designing robots that collaborate safely with humans.

Delivered as an instructor-led live session (available online or onsite), this program targets beginner to intermediate learners aiming to build human–robot interaction systems that improve usability, safety, and overall user experience.

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

• Grasp the foundational concepts and design principles of human–robot interaction.
• Create voice-based control and response mechanisms for robotic systems.
• Apply computer vision techniques to implement gesture recognition.
• Design collaborative control systems that ensure safe and shared autonomy.
• Assess HRI systems according to usability, safety standards, and human factors.

Course Format

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

Customization Options

• For tailored training arrangements for this course, please reach out to us.

Industrial Robotics Automation: Integrating ROS with PLCs and 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 to achieve synchronized operations, while exploring digital twin environments to simulate, monitor, and optimize production processes. The curriculum emphasizes interoperability, real-time control, and predictive analysis through the use of digital replicas of physical systems.

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

By the conclusion of this training, participants will be capable of:

• Understanding the communication protocols used between ROS and PLC systems.
• Implementing real-time data exchange mechanisms between robots and industrial controllers.
• Developing digital twins for monitoring, testing, and process simulation.
• Integrating sensors, actuators, and robotic manipulators into industrial workflows.
• Designing and validating industrial automation systems using hybrid simulation environments.

Course Format

• Interactive lectures and architecture walkthroughs.
• Hands-on exercises focused on integrating ROS and PLC systems.
• Implementation of simulation and digital twin projects.

Course Customization Options

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

This advanced course integrates robotic control principles with contemporary machine learning techniques, focusing on Robotic Manipulation and Grasping with Deep Learning. Participants will investigate how deep learning enhances robotic capabilities in areas such as perception, motion planning, and dexterous grasping. Through a combination of theoretical study, simulation, and practical coding, the curriculum guides learners from perception-based control mechanisms to end-to-end policy learning for complex manipulation tasks.

Delivered by an instructor either online or onsite, this training is designed for advanced professionals seeking to leverage deep learning methods to create intelligent, adaptable, and precise robotic manipulation systems.

Upon completion, participants will be able to:

• Create perception models for object recognition and pose estimation.
• Train neural networks to detect grasps and plan motion paths.
• Combine deep learning components with robotic controllers using ROS 2.
• Simulate and assess grasping and manipulation strategies within virtual environments.
• Deploy and refine learned models on actual or simulated robotic arms.

Course Format

• Expert-led lectures and detailed analysis of algorithms.
• Practical coding and simulation exercises.
• Project-based implementation and evaluation.

Customization Options

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

Multi-Robot Systems and Swarm Intelligence is an advanced training course that examines 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 blends theory with practical simulation to prepare learners for applications in logistics, defense, search and rescue, and autonomous exploration.

This instructor-led, live training (available online or onsite) is designed for advanced-level professionals who want 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:

• Grasp 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.

TinyML is a framework for deploying machine learning models on low-power microcontrollers and embedded platforms used in robotics and autonomous systems.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to integrate TinyML-based perception and decision-making capabilities into autonomous robots, drones, and intelligent control systems.

Upon finishing this course, participants will be able to:

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

Format of the Course

• Technical lectures combined with interactive discussions.
• Hands-on labs focusing on embedded robotics tasks.
• Practical exercises simulating real-world autonomous workflows.

Course Customization Options

• For organization-specific robotics environments, customization can be arranged upon request.

Safe & Explainable Robotics is a comprehensive training program focused on the safety, verification, and ethical governance of robotic systems. This course bridges theory and 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 line with international standards.

This instructor-led, live training (available online or onsite) is aimed at 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.

Format of the Course

• Interactive lecture and discussion.
• Hands-on simulation and safety analysis exercises.
• Case studies from real-world robotics applications.

Course Customization Options

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

Edge AI empowers artificial intelligence models to operate directly on embedded or resource-constrained devices, thereby minimizing latency and power usage while enhancing the autonomy and privacy of robotic systems.

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

Upon completing this training, participants will be capable of:

• Gaining a solid understanding of TinyML and edge AI fundamentals for robotics.
• Converting and deploying AI models for on-device inference.
• Optimizing models to improve speed, reduce size, and enhance energy efficiency.
• Integrating edge AI systems into robotic control architectures.
• Evaluating performance and accuracy in real-world scenarios.

Format of the Course

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

Course Customization Options

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

This instructor-led, live training in Argentina (online or onsite) is designed for intermediate-level participants eager to explore the role of collaborative robots (cobots) and other human-centric AI systems in modern workplaces.

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

• Grasp the fundamental principles of Human-Centric Physical AI and its practical applications.
• Examine how collaborative robots enhance workplace productivity.
• Identify and address challenges inherent in human-machine interactions.
• Design workflows that maximize collaboration between humans and AI-driven systems.
• Foster a culture of innovation and adaptability within AI-integrated workplaces.

Reinforcement learning (RL) constitutes a machine learning paradigm wherein agents acquire decision-making capabilities through interaction with their environment. Within the domain of robotics, RL empowers autonomous systems to cultivate adaptive control and decision-making faculties derived from experiential feedback.

This instructor-led live training, available online or onsite, targets advanced machine learning engineers, robotics researchers, and developers seeking to design, implement, and deploy reinforcement learning algorithms for robotic applications.

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

• Gaining a solid grasp of 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.
• Enabling robots to execute complex autonomous tasks through trial and error.
• Optimizing training performance using deep learning frameworks such as PyTorch.

Course Format

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

Customization Options

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

OpenCV serves as an open-source computer vision library facilitating real-time image processing, while deep learning frameworks like TensorFlow offer the necessary tools for intelligent perception and decision-making in robotic systems.

This instructor-led, live training (available online or onsite) targets intermediate-level robotics engineers, computer vision practitioners, and machine learning engineers seeking to apply computer vision and deep learning techniques for robotic perception and autonomy.

Upon completing this training, participants will be able to:

• Implement computer vision pipelines using OpenCV.
• Integrate deep learning models for object detection and recognition.
• Utilize vision-based data for robotic control and navigation.
• Combine classical vision algorithms with deep neural networks.
• Deploy computer vision systems on embedded and robotic platforms.

Course Format

• Interactive lectures and discussions.
• Hands-on practice with OpenCV and TensorFlow.
• Live-lab implementation on simulated or physical robotic systems.

Course Customization Options

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

This instructor-led, live training in Argentina (online or onsite) is designed for advanced robotics engineers and AI researchers aiming to utilize Multimodal AI to integrate various sensory data. The objective is to create more autonomous and efficient robots capable of seeing, hearing, and touching.

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

• Implement multimodal sensing in robotic systems.
• Develop AI algorithms for sensor fusion and decision-making.
• Create robots that can perform complex tasks in dynamic environments.
• Address challenges in real-time data processing and actuation.

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

This instructor-led live training, available online or onsite, targets advanced robotics engineers, systems integrators, and automation leaders seeking to implement AI-driven perception, planning, and control within smart manufacturing settings.

Upon completion, participants will be able to:

• Comprehend and utilize AI methods 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.
• Incorporate intelligent robotic systems into smart factory operations.

Course Format

• Engaging lectures and group discussions.
• Extensive exercises and practical practice.
• Practical implementation within a live laboratory environment.

Customization Options

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

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.

This instructor-led, live training in Argentina (online or on-site) is designed for intermediate-level participants seeking 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 integrates machine learning, control systems, and sensor fusion to develop intelligent machines capable of autonomous perception, reasoning, and action. Leveraging modern tools such as ROS 2, TensorFlow, and OpenCV, engineers can now create robots that intelligently navigate, plan, and interact with real-world environments.

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 completion of this training, participants will be able to:

• Utilize Python and ROS 2 to build and simulate robotic behaviors.
• Implement Kalman and Particle Filters for precise localization and tracking.
• Apply computer vision techniques with OpenCV for perception and object detection.
• Leverage 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 lectures and discussions.
• Hands-on implementation using ROS 2 and Python.
• Practical exercises involving simulated and real robotic environments.

Course Customization Options

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

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 human intervention.

Through this instructor-led live training, participants will gain foundational knowledge in bot development by building sample chatbots using dedicated development tools and frameworks.

Upon completing this training, participants will be able to:

• Comprehend the diverse uses and applications of bots
• Understand the end-to-end bot development process
• Explore the various tools and platforms utilized in bot construction
• Construct a sample chatbot for Facebook Messenger
• Build a sample chatbot using the Microsoft Bot Framework

Audience

• Developers aiming to create their own bots

Course Format

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

This instructor-led, live training in Argentina (online or onsite) is designed for engineers interested in understanding how artificial intelligence can be applied to mechatronic systems.

Upon completing this training, participants will be able to:

• Obtain a comprehensive overview of artificial intelligence, machine learning, and computational intelligence.
• Grasp the fundamental concepts of neural networks and various learning methodologies.
• Select appropriate artificial intelligence approaches to effectively address real-world challenges.
• Implement AI solutions within the context of mechatronic engineering.