Autonomous Navigation & SLAM with ROS 2 Training Course

This course, Artificial Intelligence (AI) for Robotics, integrates machine learning, control systems, and sensor fusion to empower engineers in creating intelligent machines that can autonomously perceive, reason, and act. Leveraging industry-standard tools such as ROS 2, TensorFlow, and OpenCV, participants will learn to design robots capable of intelligent navigation, planning, and interaction within real-world environments.

Designed for intermediate-level engineers, this instructor-led training (available online or onsite) focuses on the development, training, and deployment of AI-driven robotic systems utilizing current open-source technologies and frameworks.

Upon completion, participants will have the proficiency to:

• Construct and simulate robotic behaviors using Python and ROS 2.
• Deploy Kalman and Particle Filters to enhance localization and tracking capabilities.
• Utilize OpenCV to apply computer vision techniques for perception and object detection.
• Employ TensorFlow for motion prediction and implementing learning-based control strategies.
• Incorporate SLAM (Simultaneous Localization and Mapping) to enable autonomous navigation.
• Build reinforcement learning models to refine and improve robotic decision-making processes.

Training Methodology

• Engaging lectures complemented by interactive discussions.
• Practical, hands-on implementation sessions using ROS 2 and Python.
• Exercises that bridge the gap between simulated and real-world robotic environments.

Customization Services

For organizations seeking a tailored training experience, please contact our team to discuss custom course arrangements.

Think of a bot, or chatbot, as a digital assistant designed to streamline user interactions across various messaging platforms. It allows users to accomplish tasks efficiently without the need for human-to-human communication.

In this instructor-led live training session, participants will learn the fundamentals of bot development. By working through practical examples and utilizing established bot development tools and frameworks, you will gain hands-on experience in creating sample chatbots.

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

• Comprehend the diverse applications and use cases for bots
• Master the end-to-end process of developing a bot
• Investigate the array of tools and platforms available for bot construction
• Develop a sample chatbot tailored for Facebook Messenger
• Construct a sample chatbot leveraging the Microsoft Bot Framework

Audience

• Developers eager to design and build their own custom bots

Course Format

• A blend of lectures, discussions, and exercises with a strong emphasis on hands-on practice

Edge AI empowers artificial intelligence models to execute directly on embedded systems and resource-constrained devices. This approach significantly lowers latency and power usage, thereby enhancing the autonomy and privacy of robotic systems.

Designed for intermediate-level embedded developers and robotics engineers, this instructor-led live training (available online or onsite) focuses on implementing machine learning inference and optimization techniques directly on robotic hardware. The curriculum leverages TinyML and various edge AI frameworks to facilitate this integration.

Upon completion, participants will be equipped to:

• Master the core principles of TinyML and edge AI within a robotics context.
• Transform and deploy AI models specifically for on-device inference.
• Refine models to achieve optimal speed, compact size, and energy efficiency.
• Seamlessly integrate edge AI capabilities into robotic control architectures.
• Assess performance metrics and accuracy in practical, real-world environments.

Course Format

• Engaging lectures paired with open discussions.
• Practical hands-on sessions utilizing TinyML and edge AI toolchains.
• Applied exercises performed on embedded and robotic hardware platforms.

Customization Options

• For tailored training requirements, please reach out to us to schedule a customized session.

This live, instructor-led training in Thailand (delivered online or on-site) targets intermediate-level participants interested in understanding the contribution of collaborative robots (cobots) and other human-centric AI systems to modern workplace operations.

Upon completion, participants will be equipped to:

• Grasp the foundational principles of Human-Centric Physical AI and its real-world applications.
• Examine how collaborative robots contribute to boosting workplace efficiency.
• Recognize and mitigate complexities inherent in human-machine interactions.
• Structure workflows that foster seamless synergy between human teams and AI-driven systems.
• Cultivate an organizational culture that champions innovation and adaptability in AI-enabled environments.

Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control is a practical course that introduces participants to designing and implementing intuitive interfaces for communicating with robots. By blending theoretical frameworks, design methodologies, and coding exercises, this training enables learners to create natural and responsive interaction systems utilizing speech, gestures, and shared control mechanisms. Participants will gain the skills to integrate perception modules, build multimodal input systems, and engineer robots that collaborate safely with humans.

Delivered as a live, instructor-led session (either online or in-person), this program targets beginners through intermediate-level professionals seeking to enhance usability, safety, and user experience in human-robot interaction systems.

Upon completion, participants will be equipped to:

• Grasp the core principles and foundational concepts of human-robot interaction design.
• Create voice-driven control and feedback loops for robotic agents.
• Apply computer vision methods to implement gesture recognition capabilities.
• Architect collaborative control systems that ensure safe shared autonomy.
• Assess HRI systems by focusing on usability, safety, and human factors.

Delivery Format

• Engaging lectures supported by live demonstrations.
• Practical coding and design workshops.
• Experimental exercises conducted in simulation or physical robotic environments.

Customization Options

• For tailored training aligned with specific requirements, please reach out to schedule a consultation.

This hands-on course, Industrial Robotics Automation: ROS-PLC Integration & Digital Twins, is designed to bridge the gap between industrial automation and contemporary robotics frameworks. Learners will acquire the skills necessary to integrate ROS-based robotic systems with PLCs for synchronized operations, while also exploring digital twin environments to simulate, monitor, and optimize production processes. The curriculum places a strong emphasis on interoperability, real-time control, and predictive analysis utilizing digital replicas of physical systems.

Delivered as an instructor-led, live training session (either online or onsite), this course is tailored for intermediate-level professionals seeking to develop practical competencies in connecting ROS-controlled robots with PLC environments and implementing digital twins to enhance automation and manufacturing efficiency.

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

• Comprehend the communication protocols governing interactions between ROS and PLC systems.
• Facilitate real-time data exchange between robots and industrial controllers.
• Construct digital twins for purposes of monitoring, testing, and process simulation.
• Incorporate sensors, actuators, and robotic manipulators seamlessly into industrial workflows.
• Design and validate industrial automation systems through the use of hybrid simulation environments.

Course Format

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

Course Customization Options

• For tailored training solutions, please reach out to us to discuss your specific requirements.

This instructor-led live training, conducted in Thailand (either online or on-site), is tailored for engineers eager to investigate the application of artificial intelligence within mechatronic systems.

By the conclusion of the program, participants will be capable of:

• Gaining a broad perspective on artificial intelligence, machine learning, and computational intelligence.
• Comprehending neural network concepts and diverse learning techniques.
• Effectively selecting AI methodologies to solve practical problems.
• Applying AI technologies in mechatronic engineering projects.

This advanced training course delves into the design, coordination, and control of robotic teams, drawing inspiration from biological swarm behaviors. Participants will explore methods for modeling interactions, implementing distributed decision-making processes, and optimizing collaboration among multiple agents. By integrating theoretical concepts with hands-on simulation, the course equips learners with the skills necessary for applications in logistics, defense, search and rescue, and autonomous exploration.

Delivered as an instructor-led, live training session (either online or onsite), this program is tailored for advanced-level professionals seeking to design, simulate, and implement multi-robot and swarm-based systems utilizing open-source frameworks and algorithms.

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

• Grasp the core principles and dynamics underlying swarm intelligence and cooperative robotics.
• Develop effective communication and coordination strategies for multi-robot systems.
• Implement distributed decision-making and consensus algorithms.
• Simulate collective behaviors, including formation control, flocking, and area coverage.
• Utilize swarm-based techniques to address real-world scenarios and optimization challenges.

Course Format

• In-depth advanced lectures focusing on algorithmic concepts.
• Practical coding and simulation exercises using ROS 2 and Gazebo.
• A collaborative project applying swarm intelligence principles.

Customization Options

• Interested in a tailored training program for this course? Please contact us to make the necessary arrangements.

This instructor-led live training in Thailand (delivered online or onsite) is tailored for advanced robotics engineers and AI researchers. It focuses on applying Multimodal AI to synthesize various sensory data streams, enabling the creation of more autonomous and efficient robots with multimodal perception capabilities.

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

• Integrate multimodal sensing into robotic architectures.
• Design AI algorithms for sensor fusion and strategic decision-making.
• Develop robots that can handle complex tasks in fluctuating environments.
• Overcome challenges associated with real-time data processing and actuation.

This live, instructor-led session in Thailand (delivered online or onsite) is tailored for intermediate-level participants aiming to refine their expertise in designing, programming, and deploying intelligent robotic systems for automation and broader applications.

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

• Comprehend the principles of Physical AI and its relevance in robotics and automation.
• Design and program intelligent robotic systems suited for dynamic environments.
• Implement AI models to facilitate autonomous decision-making in robots.
• Utilize simulation tools for rigorous robotic testing and optimization.
• Tackle challenges including sensor fusion, real-time processing, and energy efficiency.

This practical course focuses on agile prototyping for robotics, leveraging the power of ROS 2 and Docker to streamline the development, testing, and deployment of robotic applications. Learners will gain the skills to containerize complex robotics environments, seamlessly integrate ROS 2 packages, and construct modular robotic systems that prioritize both reproducibility and scalability. The program places a strong emphasis on agile methodologies, version control, and collaborative workflows, making it ideal for teams in the early stages of development or those driving innovation.

Delivered as a live, instructor-led training session available both online and on-site, this course is tailored for developers with beginner to intermediate experience who are looking to enhance their robotics development workflows through the combined use of ROS 2 and Docker.

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

• Establish a ROS 2 development environment inside Docker containers.
• Create and test robotic prototypes using modular, easily reproducible configurations.
• Leverage simulation tools to verify system performance prior to physical hardware deployment.
• Enhance team collaboration through the management of containerized robotics projects.
• Implement continuous integration and deployment strategies within robotics pipelines.

Course Format

• Engaging interactive lectures paired with live demonstrations.
• Practical hands-on exercises utilizing ROS 2 and Docker environments.
• Focused mini-projects addressing real-world robotic application scenarios.

Customization Options

• For those requiring a tailored training experience for this course, please reach out to arrange specific details.

Reinforcement learning (RL) represents a key paradigm in machine learning where agents acquire decision-making skills through interaction with their environment. In the field of robotics, this approach empowers autonomous systems to cultivate adaptive control and strategic decision-making abilities by leveraging experience and feedback loops.

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

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

• Grasp the core principles and mathematical foundations of reinforcement learning.
• Implement key RL algorithms, including Q-learning, DDPG, and PPO.
• Integrate RL models with robotic simulation environments using OpenAI Gym and ROS 2.
• Enable robots to execute complex tasks autonomously via iterative trial and error.
• Enhance training efficiency by utilizing deep learning frameworks such as PyTorch.

Course Format

• Engaging lectures coupled with interactive discussions.
• Practical implementation using Python, PyTorch, and OpenAI Gym.
• Applied exercises in both simulated and physical robotic environments.

Course Customization Options

• For tailored training requirements, please reach out to our team to discuss arrangements.

This advanced program connects the principles of robotic control with cutting-edge machine learning methodologies. Learners will investigate how deep learning enhances perception, motion planning, and dexterous grasping within robotic architectures. Combining theoretical foundations, simulation, and practical coding tasks, the curriculum guides students from perception-driven control to end-to-end policy learning for complex manipulation duties.

Delivered as an instructor-led, live training session (either online or onsite), this course is designed for advanced professionals seeking to implement deep learning strategies for intelligent, adaptive, and precise robotic manipulation.

Upon completion, participants will be equipped to:

• Construct perception models for identifying objects and estimating poses.
• Train neural networks to detect grasps and plan motions.
• Combine deep learning modules with robotic controllers via ROS 2.
• Test and assess grasping and manipulation techniques within virtual settings.
• Deploy and refine trained models on physical or simulated robotic arms.

Course Delivery Style

• In-depth lectures and algorithmic analysis led by experts.
• Practical coding and simulation activities.
• Project-based implementation and validation.

Customization Choices

• To arrange a tailored version of this course, please reach out to us.

Smart Robotics represents the seamless fusion of artificial intelligence with robotic systems to enhance perception capabilities, decision-making processes, and autonomous control.

This instructor-led training, available in both online and onsite formats, is specifically designed for advanced robotics engineers, systems integrators, and automation leads. The program focuses on equipping professionals with the skills to implement AI-driven perception, planning, and control within smart manufacturing environments.

Upon completion, participants will be equipped to:

• Master and implement AI techniques tailored for robotic perception and sensor fusion.
• Design motion planning algorithms suitable for both collaborative and industrial robotic applications.
• Execute learning-based control strategies to facilitate real-time decision-making.
• Seamlessly integrate intelligent robotic systems into smart factory workflows.

Course Format

• Engaging interactive lectures and group discussions.
• Extensive exercises and practical drills.
• Hands-on implementation tasks within a live-lab setting.

Customization Options

• For tailored training solutions, please contact our team to discuss specific requirements.