Human-Centric Physical AI: Collaborative Robots and Beyond Training Course

Artificial Intelligence (AI) for Robotics integrates machine learning, control systems, and sensor fusion to develop intelligent machines that can perceive, reason, and act autonomously. Utilizing modern tools such as ROS 2, TensorFlow, and OpenCV, engineers are now capable of designing robots that navigate, plan, and interact with real-world environments with advanced intelligence.

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

By the end 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 localization and tracking.
• Apply computer vision techniques using OpenCV for perception and object detection.
• Use TensorFlow for motion prediction and learning-based control.
• Integrate SLAM (Simultaneous Localization and Mapping) for 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 with simulated and real robotic environments.

Course Customization Options for Government

To request a customized training program tailored to specific needs, 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 for government and industry use.

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 for government projects.

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

• Set up and configure ROS 2 for autonomous navigation applications in governmental settings.
• Implement SLAM algorithms for mapping and localization suitable for public sector environments.
• Integrate sensors such as LiDAR and cameras with ROS 2 to enhance situational awareness in government operations.
• Simulate and test autonomous navigation in Gazebo, a tool crucial for validating systems before deployment.
• Deploy navigation stacks on physical robots used in various public sector workflows.

Format of the Course

• Interactive lecture and discussion focused on government applications.
• Hands-on practice using ROS 2 tools and simulation environments relevant to public sector needs.
• Live-lab implementation and testing on virtual or physical robots tailored for government use cases.

Course Customization Options

• To request a customized training for this course, please contact us to arrange specific modules aligned with your agency's requirements.

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

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

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

• Implement computer vision pipelines using OpenCV in alignment with government standards.
• Integrate deep learning models for object detection and recognition, ensuring compliance with regulatory requirements.
• Utilize vision-based data for robotic control and navigation in public sector environments.
• Combine classical vision algorithms with deep neural networks to optimize performance in government projects.
• Deploy computer vision systems on embedded and robotic platforms, adhering to governance and accountability protocols.

Format of the Course

• Interactive lectures and discussions focused on public sector applications.
• Hands-on practice using OpenCV and TensorFlow, tailored to government scenarios.
• Live-lab implementation on simulated or physical robotic systems relevant to government operations.

Course Customization Options

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

A chatbot or bot serves as a digital assistant designed to automate user interactions across various messaging platforms, enabling tasks to be completed more efficiently without requiring direct human-to-human communication. In this instructor-led, live training, participants will gain the skills necessary to begin developing bots by working through the creation of sample chatbots using specialized development tools and frameworks. By the end of this training, participants will be able to: - Understand the diverse applications and uses of bots - Grasp the comprehensive process involved in bot development - Examine the various tools and platforms utilized in building bots - Construct a sample chatbot for Facebook Messenger - Develop a sample chatbot using Microsoft Bot Framework **Audience** - Developers interested in creating their own bots for government or private sector use **Format of the Course** - Part lecture, part discussion, with exercises and extensive hands-on practice

Edge AI enables artificial intelligence models to run directly on embedded or resource-constrained devices, reducing latency and power consumption while enhancing autonomy and privacy in robotic systems. This instructor-led, live training (online or onsite) is designed for intermediate-level embedded developers and robotics engineers who wish to implement machine learning inference and optimization techniques directly on robotic hardware using TinyML and edge AI frameworks for government applications. By the end of this training, participants will be able to: - Understand the fundamentals of TinyML and edge AI for robotics. - Convert and deploy AI models for on-device inference. - Optimize models for speed, size, and energy efficiency. - Integrate edge AI systems into robotic control architectures. - Evaluate performance and accuracy in real-world scenarios. **Format of the Course** - Interactive lecture and discussion. - Hands-on practice using TinyML and edge AI toolchains. - Practical exercises on embedded and robotic hardware platforms. **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 designed to introduce participants to the design and implementation of intuitive interfaces for human-robot communication. This training integrates theoretical knowledge, design principles, and programming practices to create 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 to intermediate participants who wish to design and implement human-robot interaction systems that enhance usability, safety, and user experience for government applications. By the end of this training, participants will be able to: - Understand the foundational principles and design practices 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, hands-on course designed to bridge industrial automation with modern robotics frameworks. Participants will learn how to integrate ROS-based robotic systems with Programmable Logic Controllers (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 (online or onsite) is aimed at intermediate-level professionals who wish 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 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 lectures and architecture walkthroughs.
• Hands-on exercises integrating ROS and PLC systems.
• Simulation and digital twin project implementation.

Course Customization Options for Government

• To request a customized training tailored to the needs of government agencies, please contact us to arrange.

This instructor-led, live training (online or onsite) is designed for government 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 for government applications. - Understand the concepts of neural networks and various learning methods. - Select appropriate artificial intelligence approaches effectively for real-life problems in the public sector. - Implement AI applications in mechatronic engineering projects for government.

Multi-Robot Systems and Swarm Intelligence is an advanced training program that delves into the design, coordination, and control of robotic teams inspired by biological swarm behaviors. This course equips participants with the skills to model interactions, implement distributed decision-making processes, and optimize collaboration among multiple agents. The curriculum integrates theoretical knowledge with practical simulation exercises to prepare learners for applications in logistics, defense, search and rescue, and autonomous exploration. This instructor-led, live training (available online or on-site) is designed for advanced-level professionals who aim 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 in-depth algorithmic analysis. - Hands-on coding and simulation using ROS 2 and Gazebo. - Collaborative project applying swarm intelligence principles. **Course Customization Options for Government** To request a customized training for government applications, please contact us to arrange.

This instructor-led, live training (available online or on-site) is designed for advanced-level robotics engineers and AI researchers who aim to utilize Multimodal AI for government applications. The goal is to integrate various sensory data 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.

This instructor-led, live training (offered online or onsite) is designed for intermediate-level participants who seek to enhance their capabilities in designing, programming, and deploying intelligent robotic systems for government and other sectors. 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 suitable for dynamic environments. - Implement AI models to enable autonomous decision-making in robots. - Utilize simulation tools for testing and optimizing robotic performance. - Address challenges such as sensor fusion, real-time processing, and energy efficiency.

Practical Rapid Prototyping for Robotics with ROS 2 & Docker is a hands-on course designed to assist developers in building, testing, and deploying 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 for Government** - To request a customized training for this course, please contact us to arrange.

Reinforcement learning (RL) is a machine learning paradigm where agents learn to make decisions by interacting with an environment. In robotics, RL enables autonomous systems to develop adaptive control and decision-making capabilities through experience and feedback. This instructor-led, live training (online or onsite) is designed for advanced-level machine learning engineers, robotics researchers, and developers who wish to design, implement, and deploy reinforcement learning algorithms in robotic applications for government. By the end of this training, participants will be able to: - Understand the principles and mathematics of reinforcement learning. - Implement RL algorithms such as Q-learning, DDPG, and PPO. - Integrate RL with robotic simulation environments using OpenAI Gym and ROS 2. - Train robots to perform complex tasks autonomously through trial and error. - Optimize training performance using deep learning frameworks like PyTorch. **Format of the Course** - Interactive lecture and discussion. - Hands-on implementation using Python, PyTorch, and OpenAI Gym. - Practical exercises in simulated or physical robotic environments. **Course Customization Options** - To request a customized training for this course, please contact us to arrange.

Robot Manipulation and Grasping with Deep Learning is an advanced course designed to integrate robotic control with contemporary machine learning techniques. Participants will delve into how deep learning can improve perception, motion planning, and dexterous grasping in robotic systems. The course combines theoretical instruction, simulation exercises, and practical coding sessions to guide learners from perception-based control to end-to-end policy learning for manipulation tasks. This instructor-led, live training (available online or on-site) is targeted at advanced-level professionals who aim to apply deep learning methods to enable intelligent, adaptable, and precise robotic manipulation. By the end of this training, participants will be able to: - Develop perception models for object recognition and pose estimation. - Train neural networks for grasp detection and motion planning. - Integrate deep learning modules with robotic controllers using ROS 2. - Simulate and evaluate grasping and manipulation strategies in virtual environments. - Deploy and optimize 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 for Government** To request a customized training for government agencies, please contact us to arrange.

Smart Robotics involves the integration of artificial intelligence into robotic systems to enhance perception, decision-making, and autonomous control. This instructor-led, live training (available online or on-site) is designed for advanced-level robotics engineers, systems integrators, and automation leads who aim to implement AI-driven perception, planning, and control in smart manufacturing environments for government and industry applications. By the end of this training, participants will be able to: - Understand and apply AI techniques for robotic perception and sensor fusion. - Develop motion planning algorithms for collaborative and industrial robots. - Deploy learning-based control strategies for real-time decision-making. - Integrate intelligent robotic systems into smart factory workflows. **Format of the Course** - Interactive lecture and discussion - Extensive exercises and practice sessions - Hands-on implementation in a live-lab environment **Course Customization Options** - To request a customized training for this course, please contact us to arrange.