TinyML for Autonomous Systems and Robotics Training Course

Artificial Intelligence (AI) for Robotics integrates machine learning, control systems, and sensor fusion to engineer intelligent systems capable of autonomous perception, reasoning, and execution. Utilizing modern technical standards such as ROS 2, TensorFlow, and OpenCV, professionals can now design robotic systems that intelligently navigate, plan operations, and interact with physical environments.

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

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

• Utilize Python and ROS 2 to construct and simulate robotic behavioral patterns.
• Implement Kalman and Particle Filters for accurate localization and target tracking.
• Apply computer vision techniques using OpenCV for enhanced perception and object detection.
• Employ TensorFlow for motion prediction and learning-based control mechanisms.
• Integrate SLAM (Simultaneous Localization and Mapping) systems for autonomous navigation capabilities.
• Develop reinforcement learning models to enhance robotic decision-making processes.

Instructional Delivery Format

• Interactive lecturing and facilitated discussion sessions.
• Practical implementation exercises using ROS 2 and Python coding environments.
• Applied exercises in both simulated and real-world robotic operational settings.

Course Tailoring Options

To request a customized training curriculum for this course, please contact the relevant office to coordinate arrangements.

This instructor-led, live training in US provides intermediate robotics engineers with practical proficiency in autonomous navigation and SLAM using ROS 2. Participants will acquire the skills to integrate LiDAR sensors, implement mapping algorithms, conduct simulations in Gazebo, and deploy real-world navigation stacks on physical robots for government applications.

This instructor-led training in US addresses computer vision for robotics using OpenCV and deep learning. Participants will acquire the skills to implement perception pipelines, integrate detection models, and deploy systems on embedded platforms for autonomous control for government operations.

An automated conversational agent, or chatbot, functions as a digital assistant designed to streamline interactions across multiple messaging channels. It enhances operational efficiency by enabling rapid task completion without direct human intervention.

This instructor-led, hands-on training program provides participants with the necessary skills to initiate bot development. Learners will progress through the creation of sample chatbots by utilizing established development tools and frameworks suitable for technical implementation.

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

• Identify diverse operational applications and use cases for automated agents
• Master the end-to-end lifecycle of bot development
• Evaluate the array of tools and platforms available for constructing bots
• Develop a prototype chatbot for the Facebook Messenger platform
• Construct a sample chatbot utilizing the Microsoft Bot Framework

Intended Audience

• Software developers seeking to build custom automation solutions

Course Delivery Method

• A blend of instructional lectures, collaborative discussions, guided exercises, and extensive practical application

This live training in US guides intermediate engineers in deploying TinyML models for robotics. Participants will learn to optimize inference for speed and energy, integrate AI into control systems, and build autonomous, low-latency robotic solutions directly on embedded hardware, ensuring efficiency for government operations.

This instructor-led, live training in US (available online or on-site) is designed for intermediate-level professionals seeking to examine the function of collaborative robots (cobots) and other human-centric AI systems within modern public and private sector workplaces.

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

• Comprehend the core principles of Human-Centric Physical AI and its practical applications.
• Analyze the contribution of collaborative robots to workforce productivity and output.
• Recognize and resolve specific issues arising from human-machine interaction.
• Construct workflows that maximize operational collaboration between staff and AI-driven systems.
• Foster a culture of innovation and adaptability within AI-integrated operational environments.

This hands-on HRI course in US covers voice, gesture, and collaborative control. It instructs participants on building intuitive, multimodal interfaces for safe human-robot collaboration, emphasizing practical design principles, speech recognition, gesture detection, and shared autonomy to improve usability and safety for government applications.

This practical training on [0] covers ROS-PLC integration and digital twins for industrial automation. It instructs participants on synchronizing robotic systems with PLCs, developing digital replicas for simulation, and optimizing production workflows through real-time data exchange and advanced monitoring techniques.

This facilitator-led training program, offered in US (online or in-person), is tailored for engineering professionals seeking to master the application of artificial intelligence to mechatronic systems for government purposes.

Upon successful completion of this course, participants will be prepared to:

• Develop a robust understanding of artificial intelligence, machine learning, and computational intelligence.
• Grasp the fundamental concepts of neural networks and diverse learning algorithms.
• Effectively identify and implement artificial intelligence strategies to solve practical operational problems.
• Integrate artificial intelligence applications within mechatronic engineering for government.

An advanced training in US covering the design and coordination of multi-robot systems for government. It models swarm intelligence, implements distributed decision-making, and optimizes multi-agent collaboration using hands-on simulation in ROS 2 and Gazebo for real-world applications.

This instructor-led live training session, conducted in US (online or on-site), targets advanced robotics engineers and AI researchers. The objective is to apply Multimodal AI to integrate diverse sensory data, resulting in more autonomous and efficient robotic systems for government use that can see, hear, and touch.

Following this training, participants will be capable of:

• Implementing multimodal sensing capabilities in robotic systems.
• Developing AI algorithms for sensor fusion and automated decision processes.
• Creating robotic systems designed to perform complex tasks within variable environments.
• Addressing technical hurdles related to real-time data processing and actuation control.

This instructor-led, live training held in US (online or on-site) is targeted at intermediate-level personnel aiming to refine their expertise in designing, programming, and deploying intelligent robotic systems for government operations and broader automation tasks.

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

• Understand the core principles of Physical AI and its specific applications in robotics and automation.
• Design and program intelligent robotic systems tailored for dynamic operational environments.
• Implement AI models to enable autonomous decision-making capabilities in robots.
• Utilize simulation tools for comprehensive robotic testing and performance optimization.
• Address critical technical issues including sensor fusion, real-time data processing, and energy efficiency.

This practical, instructor-led training in US addresses rapid prototyping for robotics using ROS 2 and Docker. Participants develop the competency to build, test, and deploy modular robotic systems efficiently, gaining practical skills in containerization, simulation, and version control to support scalable development for government initiatives.

This instructor-led live training in US addresses reinforcement learning principles and advanced deep RL techniques such as PPO and DDPG. Participants will implement algorithms using Python and PyTorch, integrate them with ROS 2 and OpenAI Gym, and train autonomous robots to resolve complex tasks for government operations.

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

This instructor-led training program, available online or on-site, is designed for advanced robotics engineers, systems integrators, and automation leaders seeking to implement AI-driven perception, planning, and control solutions within smart manufacturing environments.

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

• Apply artificial intelligence techniques for robust robotic perception and sensor data fusion.
• Engineer motion planning algorithms suitable for collaborative and industrial robotic applications.
• Implement learning-based control strategies to facilitate real-time decision-making.
• Integrate intelligent robotic systems into optimized smart factory workflows.

Course Delivery Format

• Interactive instructional sessions and technical discussions.
• Extensive practical exercises and skill-building activities.
• Hands-on implementation within a live laboratory environment.

Course Customization Options

• To request a tailored training curriculum aligned with specific organizational needs, please contact us to coordinate.