Reno, NV – Regus - Downtown

200 S. Virginia Street, 8th Floor, Reno, United States, 89501

The venue is in the heart of downtown Reno at 200 South Virginia, in a Class‑A office tower with easy car access via I‑80 and US 395, and abundant covered and surface parking on-site. From Reno–Tahoe International Airport (RNO), about 5.5 miles away, a taxi or rideshare takes roughly 10–15 minutes via US‑395 South and I‑80 West. Public transit is convenient: RTC bus routes run along South and North Virginia Streets, as well as along First Street, and the Amtrak station is within walking distance—making the site accessible for attendees without cars.

Carson City, NV

800 College Parkway, Carson City, United States, 89706

The venue is easily accessible by car via Highway 50 and College Parkway, with free on-site parking available. For those using public transportation, the location is served by the RTC bus system, with Route 6 offering convenient stops along College Parkway. Travelers arriving from Reno–Tahoe International Airport (RNO) can reach the venue in approximately 35 minutes by taking US‑395 and US‑50 west. For local arrivals, Carson City Airport (CXP) is just a short 10-minute drive away via College Parkwa
NobleProg Training Place, City Reno, NV - Regus - Mountain View Corporate

Reno, NV - Regus - Mountain View Corporate

5470 Kietzke Ln, Reno, United States, 89511

The venue is easily accessible by car via I‑580 and Highway 395, with free on-site parking available. For those arriving by air, Reno–Tahoe International Airport is located approximately 30 miles north, while the Carson City Airport offers local flight options and is just 5 miles away. Public transportation is also available through RTC Regional Bus routes, which have stops near the venue. From Carson City Airport (CXP), the location is only a 10-minute drive via College Parkway, providing convenient local access.

Explore Our Courses

Edge AI for Robots: TinyML, On-Device Inference & Optimization

 21 Hours

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.
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Human-Centric Physical AI: Collaborative Robots and Beyond

 14 Hours

This instructor-led, live training in Nevada (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.
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Robot Learning & Reinforcement Learning in Practice

 21 Hours

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.
Read more...

Computer Vision for Robotics: Perception with OpenCV & Deep Learning

 21 Hours

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.
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Multimodal AI in Robotics

 21 Hours

This instructor-led, live training in Nevada (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.
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Autonomous Navigation & SLAM with ROS 2

 21 Hours

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.
Read more...

Physical AI for Robotics and Automation

 21 Hours

This instructor-led, live training in Nevada (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.
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Artificial Intelligence (AI) for Robotics

 21 Hours

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.

Read more...

AI and Robotics for Nuclear - Extended

 120 Hours

In this instructor-led, live training in Nevada (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.
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AI and Robotics for Nuclear

 80 Hours

In this instructor-led, live training in Nevada (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.
Read more...

Artificial Intelligence (AI) for Mechatronics

 21 Hours

This instructor-led, live training in Nevada (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.
Read more...

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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

Upcoming Courses

Robot Learning & Reinforcement Learning in Practice

2025-12-01 9:30 am
21 hours
Reno, NV – Regus - Downtown
$ 4054 (Online)
$ 4054 (Classroom)

AI and Robotics for Nuclear - Extended

2025-12-15 9:30 am
120 hours
Reno, NV – Regus - Downtown
$ 24324 (Online)
$ 24324 (Classroom)

Edge AI for Robots: TinyML, On-Device Inference & Optimization

2025-12-29 9:30 am
21 hours
Reno, NV – Regus - Downtown
$ 4054 (Online)
$ 4054 (Classroom)

Artificial Intelligence (AI) for Mechatronics

2026-01-12 9:30 am
21 hours
Reno, NV – Regus - Downtown
$ 4054 (Online)
$ 4054 (Classroom)

Computer Vision for Robotics: Perception with OpenCV & Deep Learning

2026-01-26 9:30 am
21 hours
Reno, NV – Regus - Downtown
$ 4054 (Online)
$ 4054 (Classroom)

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