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 Nepal — 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
Nepal, Kathmandu - Classroom
near Soaltee, Tahachal Marg, Kathmandu, Nepal, 44600
Set in Kathmandu, this classroom is well located near Tahachal Marg with all amenities and WiFi.
For Sales Enquires and Meetings
All our centres have batches running on weekdays and weekends hence, please note that, in most cases, usually we are not able to organise ad hoc sales meetings, especially on our classrooms as they are all occupied with ongoing training sessions . Please contact us by e-mail or phone at least one day earlier to make an appointment with one of our consultants at our corporate offices.
Nepal, Thamel, KTM - Classroom
near Radisson , Ward 2, Kathmandu, Nepal, 44600
Set in Kathmandu, this classroom is well located near Thamel, with all amenities and WiFi.
For Sales Enquires and Meetings
All our centres have batches running on weekdays and weekends hence, please note that, in most cases, usually we are not able to organise ad hoc sales meetings, especially on our classrooms as they are all occupied with ongoing training sessions . Please contact us by e-mail or phone at least one day earlier to make an appointment with one of our consultants at our corporate offices.
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 with efficiency. Participants will learn to containerize robotics environments, integrate ROS 2 packages, and prototype modular robotic systems using Docker to ensure reproducibility and scalability. The course focuses on agility, version control, and collaborative practices ideal for early-stage development and innovation teams.
This instructor-led, live training (available online or onsite) targets beginner to intermediate-level participants looking to accelerate their robotics development workflows using ROS 2 and Docker.
Upon completion of this training, participants will be able to:
Configure a ROS 2 development environment within Docker containers.
Develop and test robotic prototypes in modular and reproducible setups.
Utilize simulation tools to validate system behavior prior to hardware deployment.
Collaborate effectively through containerized robotics projects.
Apply continuous integration and deployment concepts within robotics pipelines.
Course Format
Interactive lectures and demonstrations.
Hands-on exercises involving ROS 2 and Docker environments.
Mini-projects focused on real-world robotic applications.
Course Customization Options
To request customized training for this course, please reach out to us to arrange.
Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control is a practical course designed to familiarise participants with the design and implementation of intuitive interfaces for human–robot communication. The training blends theoretical knowledge, design principles, and programming practice to help 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 (available 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:
Grasp 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.
This course, titled "Industrial Robotics Automation: Integrating ROS with PLCs and Digital Twins," provides practical, hands-on training designed to bridge the gap between industrial automation and contemporary robotics frameworks. Participants will learn how to synchronize ROS-based robotic systems with PLCs for cohesive operations and delve into digital twin environments to simulate, monitor, and enhance production processes. The curriculum places significant emphasis on interoperability, real-time control, and predictive analysis through the use of digital replicas of physical systems.
Delivered as an instructor-led, live session (available either online or at your location), this training targets intermediate-level professionals keen on developing practical expertise in linking ROS-controlled robots with PLC environments and deploying digital twins for automation and manufacturing optimization.
Upon completion of this training, participants will be equipped to:
Comprehend the communication protocols facilitating interaction between ROS and PLC systems.
Execute real-time data exchange between robots and industrial controllers.
Create digital twins for monitoring, testing, and simulating processes.
Seamlessly integrate sensors, actuators, and robotic manipulators into industrial workflows.
Design and validate industrial automation systems using hybrid simulation environments.
Course Format
Interactive lectures and detailed architecture walkthroughs.
Practical exercises focused on integrating ROS and PLC systems.
Implementation of simulation and digital twin projects.
Course Customization Options
For those interested in tailored training for this course, please reach out to us to make arrangements.
"Robotic Manipulation and Grasping with Deep Learning" is an advanced program that connects robotic control with contemporary machine learning methodologies. Participants will investigate how deep learning can improve perception, motion planning, and dexterous grasping capabilities in robotic systems. Through a blend of theoretical concepts, simulation, and practical coding exercises, the course leads learners from perception-based control to end-to-end policy learning for manipulation tasks.
This instructor-led, live training (available online or onsite) targets advanced professionals who aim to leverage deep learning methods to achieve intelligent, adaptable, and precise robotic manipulation.
Upon completing this training, participants will be able to:
Build 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 actual or simulated robotic arms.
Course Format
Expert-led lectures and deep dives into algorithms.
Practical coding and simulation exercises.
Project-based implementation and testing.
Customization Options
To request customized training for this course, please get in touch with us.
Multi-Robot Systems and Swarm Intelligence is an advanced training program that delves into the design, coordination, and control of robotic teams, drawing inspiration from biological swarm behaviors. Participants will learn to model interactions, implement distributed decision-making processes, and optimize collaboration among multiple agents. This course blends theoretical knowledge with practical simulation exercises to prepare learners for applications in logistics, defense, search and rescue operations, and autonomous exploration.
This instructor-led live training (available online or onsite) 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.
Upon completion of this training, participants will be able to:
Grasp the principles and dynamics of swarm intelligence and cooperative robotics.
Develop 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 tasks.
Apply swarm-based techniques to real-world scenarios and optimization challenges.
Format of the Course
Advanced lectures with in-depth algorithmic analysis.
Hands-on coding and simulation using ROS 2 and Gazebo.
TinyML serves as a framework for deploying machine learning models onto low-power microcontrollers and embedded platforms within robotics and autonomous systems.
This instructor-led live training, available online or onsite, targets advanced professionals seeking to incorporate TinyML-based perception and decision-making capabilities into autonomous robots, drones, and intelligent control systems.
After completing this course, participants will be equipped to:
Design optimized TinyML models tailored 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 allows artificial intelligence models to execute directly on embedded or resource-limited devices, thereby cutting down latency and power usage while boosting autonomy and privacy in robotic systems.
This instructor-led live training (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 using TinyML and edge AI frameworks.
By the conclusion of this training, participants will be capable of:
Gaining a solid grasp of TinyML and edge AI fundamentals for robotics.
Converting and deploying AI models for on-device inference.
Optimizing models for speed, size, and energy efficiency.
Integrating edge AI systems into robotic control architectures.
Evaluating performance and accuracy in real-world scenarios.
Course Format
Interactive lectures and discussions.
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 Nepal (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 principles of Human-Centric Physical AI and its practical applications.
Examine how collaborative robots contribute to enhancing workplace productivity.
Identify and tackle challenges associated with human-machine interactions.
Design workflows that optimize collaboration between humans and AI-driven systems.
Foster a culture of innovation and adaptability in AI-integrated workplaces.
Reinforcement learning (RL) represents a machine learning approach where agents acquire decision-making capabilities through interaction with their environment. In the field of robotics, RL empowers autonomous systems to develop adaptive control and decision-making skills via experience and feedback.
This instructor-led live training, available either online or on-site, is designed for advanced machine learning engineers, robotics researchers, and developers who intend to design, implement, and deploy reinforcement learning algorithms in robotic applications.
Upon completion of this training, participants will be able to:
Grasp the fundamental principles and mathematical underpinnings 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 execute complex tasks autonomously through trial and error.
Optimize training performance utilizing deep learning frameworks like PyTorch.
Format of the Course
Interactive lectures and discussions.
Hands-on implementation using Python, PyTorch, and OpenAI Gym.
Practical exercises within simulated or physical robotic environments.
Course Customization Options
To request a customized training session for this course, please contact us to arrange details.
OpenCV serves as an open-source computer vision library that facilitates real-time image processing, while deep learning frameworks like TensorFlow offer the necessary tools for intelligent perception and decision-making within robotic systems.
This instructor-led live training, available either online or onsite, targets robotics engineers, computer vision experts, and machine learning professionals at an intermediate level who aim to leverage computer vision and deep learning techniques for robotic autonomy and perception.
Upon completion of this training, participants will be capable of:
This instructor-led, live training in Nepal (online or onsite) targets advanced robotics engineers and AI researchers seeking to apply Multimodal AI. The objective is to merge various sensory data streams to construct robots that are more autonomous and efficient, possessing the ability to see, hear, and touch.
By the conclusion 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 involves the seamless integration of artificial intelligence into robotic systems to enhance perception, decision-making, and autonomous control capabilities.
This instructor-led live training, available online or at your premises, is designed for advanced robotics engineers, systems integrators, and automation leads who aim to implement AI-driven perception, planning, and control within smart manufacturing settings.
Upon completing this training, participants will be equipped to:
Comprehend and apply AI techniques for robotic perception and sensor fusion.
Design motion planning algorithms for both collaborative and industrial robots.
Deploy learning-based control strategies to enable real-time decision-making.
Integrate intelligent robotic systems into smart factory workflows effectively.
Course Format
Engaging lectures and interactive discussions.
Ample exercises and practical practice sessions.
Hands-on implementation within a live laboratory environment.
Customization Options
For tailored training requirements, please reach out to us to make the necessary arrangements.
ROS 2 (Robot Operating System 2) is an open-source framework crafted to facilitate the creation of complex and scalable robotic applications.
This instructor-led live training, available both online and onsite, targets intermediate-level robotics engineers and developers looking to implement autonomous navigation and SLAM (Simultaneous Localization and Mapping) with ROS 2.
Upon completion of this training, participants will be capable of:
Setting up and configuring ROS 2 for autonomous navigation applications.
Implementing SLAM algorithms for accurate mapping and localization.
Integrating sensors, such as LiDAR and cameras, with ROS 2.
Simulating and testing autonomous navigation within Gazebo.
Deploying navigation stacks onto physical robots.
Format of the Course
Interactive lectures and discussions.
Practical exercises using ROS 2 tools and simulation environments.
Live-lab implementation and testing on virtual or physical robots.
Course Customization Options
To arrange customized training for this course, please get in touch with us.
This instructor-led, live training in Nepal (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 integrates machine learning, control systems, and sensor fusion to build intelligent machines that can perceive, reason, and act autonomously. Leveraging modern tools such as ROS 2, TensorFlow, and OpenCV, engineers are now able to design robots that navigate, plan, and interact with real-world environments in a smart manner.
This instructor-led, live training session (available online or onsite) is tailored for intermediate-level engineers who aim to develop, train, and deploy AI-driven robotic systems using contemporary open-source technologies and frameworks.
Upon completion of this training, participants will be able to:
Utilise Python and ROS 2 to construct and simulate robotic behaviours.
Implement Kalman and Particle Filters for precise localisation and tracking.
Apply computer vision techniques via OpenCV for perception and object detection.
Use TensorFlow for motion prediction and learning-based control.
Integrate SLAM (Simultaneous Localisation 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 Customisation Options
To request customised training for this course, please get in touch with us to arrange.
In this instructor-led live training in Nepal (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.
Azure Bot Service integrates the capabilities of the Microsoft Bot Framework with Azure Functions, offering a robust platform for rapidly constructing intelligent chatbots.
In this instructor-led live training, attendees will learn how to efficiently develop smart bots using Microsoft Azure.
Upon completion of the training, participants will be able to:
Comprehend the fundamental principles behind intelligent bots.
Develop smart bots using cloud-based applications.
Acquire practical expertise in the Microsoft Bot Framework, the Bot Builder SDK, and Azure Bot Service.
Implement proven bot design patterns in real-world situations.
Create and deploy their first intelligent bot using Microsoft Azure.
Target Audience
This course is tailored for developers, hobbyists, engineers, and IT professionals keen on bot development.
Training Format
The training blends lectures and discussions with exercises, placing a strong emphasis on hands-on practice.
A bot, or chatbot, acts as a virtual assistant designed to automate user interactions across various messaging channels, enabling faster task completion without requiring direct human intervention.
In this instructor-led live training, participants will learn the fundamentals of bot development by creating sample chatbots using industry-standard tools and frameworks.
Upon completing this training, participants will be able to:
Grasp the diverse use cases and applications of bots
Understand the end-to-end process of bot development
Explore the variety of tools and platforms available for building bots
Construct a sample chatbot for Facebook Messenger
Develop a sample chatbot using the Microsoft Bot Framework
Audience
Developers keen on creating their own bots
Course Format
A blend of lectures, discussions, exercises, and extensive hands-on practice
This instructor-led, live training in Nepal (online or onsite) is designed for 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 a comprehensive overview of artificial intelligence, machine learning, and computational intelligence.
Grasp the concepts of neural networks and various learning methodologies.
Select appropriate artificial intelligence approaches for addressing real-world problems.
Implement AI applications within the field of 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 (2)
Supply of the materials (virtual machine) to get straight into the excersises, and the explanation of the Ros2 core. Why things work a certain way.
Arjan Bakema
Course - Autonomous Navigation & SLAM with ROS 2
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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