AI for Robotics represents the meeting point between intelligence and motion — where algorithms think, sensors perceive, and machines act with purpose. It’s the frontier where data becomes dexterity, powering the next generation of autonomous systems, industrial robots, and intelligent machines.
In these instructor-led live training courses, participants explore how artificial intelligence transforms robotics into adaptive, learning systems. Through hands-on exercises, they dive into perception models, motion planning, reinforcement learning, and AI-driven control architectures that bring machines closer to human-like responsiveness.
Those joining online enter an environment that mirrors the pace of real labs — guided step by step through live demonstrations and collaborative coding via an interactive remote desktop. Every session unfolds as a shared exploration of logic and movement, not a one-way lecture.
For teams who prefer to build and test side by side, onsite live training in Dartmouth — held at customer premises or within NobleProg corporate training centers — transforms learning into experimentation. Robots, code, and imagination meet in a practical space where theory takes physical form.
Also known as Robotics AI or Intelligent Robotics, our training helps professionals bridge software and mechanics — building systems that sense, decide, and act with increasing autonomy and precision.
NobleProg — Your Local Training Provider
Dartmouth - Metropolitan Place
99 Wyse Road, Dartmouth, Canada, B3A 4S5
Metropolitan Place is an ocean-front building, close to Alderney Landing and home to leading businesses. Motorway exits and the local ferry terminal are located close by, and restaurants cafes are easily accessible for lunch.
Practical Rapid Prototyping for Robotics with ROS 2 & Docker is a hands-on course designed to help developers build, test, and deploy 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
To request a customized training for this course, please contact us to arrange.
Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control is a hands-on course designed to introduce participants to the design and implementation of intuitive interfaces for human–robot communication. The training combines theory, design principles, and programming practice to build 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-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 hands-on course focused on bridging industrial automation with modern robotics frameworks. Participants will learn 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 (online or onsite) is aimed at intermediate-level professionals who wish to build 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 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.
Robot Manipulation and Grasping with Deep Learning is an advanced course that bridges robotic control with modern machine learning techniques. Participants will explore how deep learning can enhance perception, motion planning, and dexterous grasping in robotic systems. Through theory, simulation, and practical coding exercises, the course guides learners from perception-based control to end-to-end policy learning for manipulation tasks.
This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish 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 lecture and algorithmic deep dives.
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.
Multi-Robot Systems and Swarm Intelligence is an advanced training course that explores 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 combines theory with hands-on simulation to prepare learners for applications in logistics, defense, search and rescue, and autonomous exploration.
This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish 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 algorithmic deep dives.
Hands-on coding and simulation in ROS 2 and Gazebo.
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.
Safe & Explainable Robotics is a comprehensive training focused on the safety, verification, and ethical governance of robotic systems. The 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 (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 enables artificial intelligence models to run directly on embedded or resource-constrained devices, reducing latency and power consumption while increasing autonomy and privacy in robotic systems.
This instructor-led, live training (online or onsite) is aimed at 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.
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.
This instructor-led, live training in Dartmouth (online or onsite) is aimed at intermediate-level participants who wish to explore the role of collaborative robots (cobots) and other human-centric AI systems in modern workplaces.
By the end of this training, participants will be able to:
Understand the principles of Human-Centric Physical AI and its applications.
Explore the role of collaborative robots in enhancing workplace productivity.
Identify and address challenges in human-machine interactions.
Design workflows that optimize collaboration between humans and AI-driven systems.
Promote a culture of innovation and adaptability in AI-integrated workplaces.
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 aimed at advanced-level machine learning engineers, robotics researchers, and developers who wish to design, implement, and deploy reinforcement learning algorithms in robotic applications.
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.
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:
Implement computer vision pipelines using OpenCV.
Integrate deep learning models for object detection and recognition.
Use 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.
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 Dartmouth (online or onsite) is aimed at advanced-level robotics engineers and AI researchers who wish to utilize Multimodal AI for integrating various sensory data to create more autonomous and efficient robots that can see, hear, and touch.
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.
Smart Robotics is the integration of artificial intelligence into robotic systems for improved perception, decision-making, and autonomous control.
This instructor-led, live training (online or onsite) is aimed at advanced-level robotics engineers, systems integrators, and automation leads who wish to implement AI-driven perception, planning, and control in smart manufacturing environments.
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.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.
Course Customization Options
To request a customized training for this course, 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.
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 Dartmouth (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 combines machine learning, control systems, and sensor fusion to create intelligent machines capable of perceiving, reasoning, and acting autonomously. Through modern tools like ROS 2, TensorFlow, and OpenCV, engineers can now design robots that navigate, plan, and interact with real-world environments intelligently.
This instructor-led, live training (online or onsite) is aimed at intermediate-level engineers who wish 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:
Use 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 improve robotic decision-making.
Format of the Course
Interactive lecture and discussion.
Hands-on implementation using ROS 2 and Python.
Practical exercises with simulated and real robotic environments.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
In this instructor-led, live training in Dartmouth (online or onsite), participants will learn the different technologies, frameworks and techniques for programming different types of robots to be used in the field of nuclear technology and environmental systems.
The 6-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge.
The target hardware for this course will be simulated in 3D through simulation software. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.
By the end of this training, participants will be able to:
Understand the key concepts used in robotic technologies.
Understand and manage the interaction between software and hardware in a robotic system.
Understand and implement the software components that underpin robotics.
Build and operate a simulated mechanical robot that can see, sense, process, navigate, and interact with humans through voice.
Understand the necessary elements of artificial intelligence (machine learning, deep learning, etc.) applicable to building a smart robot.
Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment.
Implement search algorithms and motion planning.
Implement PID controls to regulate a robot's movement within an environment.
Implement SLAM algorithms to enable a robot to map out an unknown environment.
Extend a robot's ability to perform complex tasks through Deep Learning.
Test and troubleshoot a robot in realistic scenarios.
In this instructor-led, live training in Dartmouth (online or onsite), participants will learn the different technologies, frameworks and techniques for programming different types of robots to be used in the field of nuclear technology and environmental systems.
The 4-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge.
The target hardware for this course will be simulated in 3D through simulation software. The code will then be loaded onto physical hardware (Arduino or other) for final deployment testing. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.
By the end of this training, participants will be able to:
Understand the key concepts used in robotic technologies.
Understand and manage the interaction between software and hardware in a robotic system.
Understand and implement the software components that underpin robotics.
Build and operate a simulated mechanical robot that can see, sense, process, navigate, and interact with humans through voice.
Understand the necessary elements of artificial intelligence (machine learning, deep learning, etc.) applicable to building a smart robot.
Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment.
Implement search algorithms and motion planning.
Implement PID controls to regulate a robot's movement within an environment.
Implement SLAM algorithms to enable a robot to map out an unknown environment.
Test and troubleshoot a robot in realistic scenarios.
A bot or chatbot is like a computer assistant that is used to automate user interactions on various messaging platforms and get things done faster without the need for users to speak to another human.
In this instructor-led, live training, participants will learn how to get started in developing a bot as they step through the creation of sample chatbots using bot development tools and frameworks.
By the end of this training, participants will be able to:
Understand the different uses and applications of bots
Understand the complete process in developing bots
Explore the different tools and platforms used in building bots
Build a sample chatbot for Facebook Messenger
Build a sample chatbot using Microsoft Bot Framework
Audience
Developers interested in creating their own bot
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
This instructor-led, live training in Dartmouth (online or onsite) is aimed at 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.
A Smart Robot is an Artificial Intelligence (AI) system that can learn from its environment and its experience and build on its capabilities based on that knowledge. Smart Robots can collaborate with humans, working along-side them and learning from their behavior. Furthermore, they have the capacity for not only manual labor, but cognitive tasks as well. In addition to physical robots, Smart Robots can also be purely software based, residing in a computer as a software application with no moving parts or physical interaction with the world.
In this instructor-led, live training, participants will learn the different technologies, frameworks and techniques for programming different types of mechanical Smart Robots, then apply this knowledge to complete their own Smart Robot projects.
The course is divided into 4 sections, each consisting of three days of lectures, discussions, and hands-on robot development in a live lab environment. Each section will conclude with a practical hands-on project to allow participants to practice and demonstrate their acquired knowledge.
The target hardware for this course will be simulated in 3D through simulation software. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.
By the end of this training, participants will be able to:
Understand the key concepts used in robotic technologies
Understand and manage the interaction between software and hardware in a robotic system
Understand and implement the software components that underpin Smart Robots
Build and operate a simulated mechanical Smart Robot that can see, sense, process, grasp, navigate, and interact with humans through voice
Extend a Smart Robot's ability to perform complex tasks through Deep Learning
Test and troubleshoot a Smart Robot in realistic scenarios
Audience
Developers
Engineers
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
Note
To customize any part of this course (programming language, robot model, etc.) please contact us to arrange.
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Testimonials (1)
its knowledge and utilization of AI for Robotics in the Future.
Ryle - PHILIPPINE MILITARY ACADEMY
Course - Artificial Intelligence (AI) for Robotics
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