AI for Robotics represents the intersection of intelligence and motion, where algorithms process information, sensors capture data, and machines execute tasks with purpose. This field is at the forefront of transforming data into dexterity, driving the development of the next generation of autonomous systems, industrial robots, and intelligent machinery.
In these instructor-led live training courses, participants delve into how artificial intelligence evolves robotics into adaptive, learning systems. Through practical exercises, they explore perception models, motion planning, reinforcement learning, and AI-driven control architectures that enhance machine responsiveness to near-human levels.
Those joining online experience an environment that replicates the pace of real labs, guided step by step through live demonstrations and collaborative coding via an interactive remote desktop. Each session is 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 Austin — held at customer premises or within Govtra corporate training centers — transforms learning into hands-on experimentation. Robots, code, and creativity converge in a practical setting where theory is brought to life.
Also known as Robotics AI or Intelligent Robotics, our training programs help professionals integrate software and mechanics, developing systems that can sense, decide, and act with increasing autonomy and precision, tailored for government applications.
Govtra — Your Local Training Provider
Austin, TX – Regus at Dobie Center
2021 Guadalupe Street, Suite 260, Austin, United States, 78705
The venue is located in the vibrant West Campus neighborhood near the University of Texas at the Dobie Center, easily reachable by car via I‑35 or I‑35 Frontage Road and Loop 1, with on-site and street parking nearby. From Austin–Bergstrom International Airport (AUS), about 11 miles southeast, a taxi or rideshare takes around 20–25 minutes via TX‑71 West and I‑35 North. Public transit users can take Capital Metro routes to the University of Texas/Dobie Mall stop directly in front of the building, placing it within a short walk for attendees without cars.
Austin, TX - Regus - Littlefield Congress
106 E 6th St, Austin, United States, 78701
The venue is conveniently situated near I‑35 and US‑183, making it easily accessible by car, with both public and garage parking options—such as the Littlefield Garage at 508 Brazos—located within walking distance. For those using public transportation, the venue is just a short walk from the Downtown Station (CapMetro) and is served by several bus routes. Travelers arriving by air will find that Austin-Bergstrom International Airport is approximately 8 miles away, providing straightforward access to and from the venue.
Practical Rapid Prototyping for Robotics with ROS 2 and 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 within the public sector.
This instructor-led, live training (online or onsite) is aimed at beginner to intermediate level participants who wish to accelerate robotics development workflows using ROS 2 and Docker for government projects.
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 for government applications.
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 for government use cases.
Course Customization Options
To request a customized training for this course, please contact Govtra 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-level 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 concepts 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 Govtra to arrange.
Industrial Robotics Automation: ROS-PLC Integration & Digital Twins is a hands-on course designed to bridge industrial automation with modern robotics frameworks. Participants will learn how 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 develop practical skills in connecting ROS-controlled robots with PLC environments and implementing digital twins for automation and manufacturing optimization for government applications.
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
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 methodologies. Participants will delve into how deep learning can improve perception, motion planning, and dexterous grasping in robotic systems. Through a combination of theoretical instruction, simulation exercises, and practical coding tasks, 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 tailored for advanced-level professionals who aim to apply deep learning techniques to achieve intelligent, adaptable, and precise robotic manipulation in their work environments.
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 for Government
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 program that delves into 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 among multiple agents. The course integrates theoretical knowledge 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 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.
TinyML is a framework designed for deploying machine learning models on low-power microcontrollers and embedded platforms utilized in robotics and autonomous systems.
This instructor-led, live training (available online or onsite) is targeted at advanced-level professionals who aim to integrate TinyML-based perception and decision-making capabilities into autonomous robots, drones, and intelligent control systems for government applications.
Upon completing this course, participants will be able to:
Design optimized TinyML models tailored for robotics applications.
Implement on-device perception pipelines to support 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 to enhance understanding.
Hands-on labs focusing on embedded robotics tasks to provide practical experience.
Practical exercises simulating real-world autonomous workflows to ensure readiness for deployment.
Course Customization Options
For organization-specific robotics environments, customization can be arranged upon request to align with specific needs and objectives.
Safe & Explainable Robotics is a comprehensive training program designed to address the safety, verification, and ethical governance of robotic systems. The course integrates theory with practical applications by examining safety case methodologies, hazard analysis, and explainable AI approaches that ensure transparent and trustworthy robotic decision-making. Participants will learn how to achieve compliance, verify system behaviors, and document safety assurance in accordance with international standards.
This instructor-led, live training (online or onsite) is targeted at intermediate-level professionals who aim to apply verification, validation, and explainability principles to ensure the safe and ethical deployment of robotic systems for government and other public sector entities.
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 Govtra to arrange.
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 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 for government applications.
By the end of this training, participants will be able to:
Understand the fundamentals of TinyML and edge AI for robotics in public sector contexts.
Convert and deploy AI models for on-device inference in government environments.
Optimize models for speed, size, and energy efficiency to meet public sector requirements.
Integrate edge AI systems into robotic control architectures for government operations.
Evaluate performance and accuracy in real-world scenarios relevant to government missions.
Format of the Course
Interactive lecture and discussion tailored for government audiences.
Hands-on practice using TinyML and edge AI toolchains adapted for public sector use.
Practical exercises on embedded and robotic hardware platforms suitable for government applications.
Course Customization Options
To request a customized training for this course, tailored to specific needs for government agencies, please contact us to arrange.
This instructor-led, live training in Austin (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 for government.
By the end of this training, participants will be able to:
Understand the principles of Human-Centric Physical AI and its applications for government.
Explore the role of collaborative robots in enhancing workplace productivity in public sector environments.
Identify and address challenges in human-machine interactions within government settings.
Design workflows that optimize collaboration between humans and AI-driven systems for government operations.
Promote a culture of innovation and adaptability in AI-integrated workplaces for government agencies.
OpenCV is an open-source computer vision library that enables real-time image processing, while deep learning frameworks such as TensorFlow provide the tools necessary for intelligent perception and decision-making in robotic systems.
This instructor-led, live training (online or onsite) is designed for intermediate-level robotics engineers, computer vision practitioners, and machine learning engineers who aim to apply computer vision and deep learning techniques for robotic perception and autonomy within their projects for government.
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.
Utilize 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 Austin (online or onsite) is designed for advanced-level robotics engineers and AI researchers who aim to leverage Multimodal AI for integrating various sensory inputs to develop 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 for government applications.
Develop AI algorithms for sensor fusion and decision-making processes.
Create robots that can perform complex tasks in dynamic environments, enhancing public sector workflows.
Address challenges related to real-time data processing and actuation, ensuring robust governance and accountability.
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 leaders who seek to implement AI-driven perception, planning, and control in smart manufacturing environments for government 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 for government use.
Format of the Course
Interactive lectures and discussions.
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 Govtra 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 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.
By the end of this training, participants will be able to:
Set up and configure ROS 2 for autonomous navigation applications in government projects.
Implement SLAM algorithms for mapping and localization in public sector environments.
Integrate sensors such as LiDAR and cameras with ROS 2 for enhanced data collection and analysis.
Simulate and test autonomous navigation scenarios using Gazebo, a high-fidelity simulation environment.
Deploy navigation stacks on physical robots to support government operations.
Format of the Course
Interactive lectures and discussions tailored for government professionals.
Hands-on practice using ROS 2 tools and simulation environments relevant to public sector workflows.
Live-lab implementation and testing on virtual or physical robots, with a focus on government applications.
Course Customization Options
To request a customized training for this course, specifically tailored for government needs, please contact us to arrange.
This instructor-led, live training in Austin (online or onsite) is designed for intermediate-level participants who wish to enhance their skills in designing, programming, and deploying intelligent robotic systems for automation and beyond, specifically for government applications.
By the end of this training, participants will be able to:
Understand the principles of Physical AI and its applications in robotics and automation within the public sector.
Design and program intelligent robotic systems for dynamic environments, ensuring alignment with government workflows and governance.
Implement AI models to enable autonomous decision-making in robots, enhancing operational efficiency and accountability for government use.
Utilize simulation tools to test and optimize robotic systems, ensuring they meet the stringent standards required for government operations.
Address challenges such as sensor fusion, real-time processing, and energy efficiency, which are critical for maintaining effective and sustainable government solutions.
Artificial Intelligence (AI) for Robotics integrates machine learning, control systems, and sensor fusion to develop intelligent machines capable of perceiving, reasoning, and acting autonomously. Utilizing modern tools such as ROS 2, TensorFlow, and OpenCV, engineers can now design 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 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.
Utilize 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 lecture and discussion.
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 for this course, please contact us to arrange.
In this instructor-led, live training in Austin (online or onsite), participants will learn the various technologies, frameworks, and techniques for programming robots used in nuclear technology and environmental systems.
The 6-week course is held 5 days a week, with each session lasting 4 hours. The daily schedule includes lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete several real-world projects applicable to their work to practice the knowledge they acquire.
The target hardware for this course will be simulated in 3D using simulation software. The ROS (Robot Operating System) open-source framework, along with C++ and Python, will be used for programming the robots.
By the end of this training, participants will be able to:
Understand key concepts in robotic technologies.
Manage the interaction between software and hardware in a robotic system.
Implement the software components that underpin robotics.
Build and operate a simulated mechanical robot capable of seeing, sensing, processing, navigating, and interacting with humans through voice commands.
Understand the essential elements of artificial intelligence (including machine learning and deep learning) applicable to building intelligent robots.
Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment.
Apply search algorithms and motion planning techniques.
Use PID controls to regulate a robot's movement within an environment.
Implement SLAM (Simultaneous Localization and Mapping) algorithms to enable a robot to map out unknown environments.
Extend a robot's capabilities to perform complex tasks through deep learning.
Test and troubleshoot a robot in realistic scenarios, ensuring it meets the necessary standards for government applications.
In this instructor-led, live training in Austin (online or onsite), participants will learn various technologies, frameworks, and techniques for programming different types of robots to be utilized in the field of nuclear technology and environmental systems.
The 4-week course is held 5 days a week. Each day consists of 4 hours of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work to practice their acquired knowledge.
The target hardware for this course will be simulated in 3D through simulation software. The code will then be deployed onto physical hardware (Arduino or other) for final 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.
Manage the interaction between software and hardware in a robotic system.
Implement the software components that underpin robotics.
Build and operate a simulated mechanical robot capable of seeing, sensing, processing, navigating, and interacting with humans through voice.
Understand and apply elements of artificial intelligence (machine learning, deep learning, etc.) to build 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, ensuring alignment with public sector workflows and governance for government applications.
The Azure Bot Service integrates the capabilities of the Microsoft Bot Framework and Azure Functions to facilitate the rapid development of intelligent bots for government.
In this instructor-led, live training, participants will learn how to efficiently create an intelligent bot using Microsoft Azure.
By the end of this training, participants will be able to:
Understand the fundamentals of intelligent bots
Learn how to develop intelligent bots using cloud applications for government
Gain proficiency in using the Microsoft Bot Framework, the Bot Builder SDK, and the Azure Bot Service
Comprehend the design principles of bots through bot patterns
Create their first intelligent bot using Microsoft Azure
Audience
Developers
Hobbyists
Engineers
IT Professionals
Format of the Course
Part lecture, part discussion, with exercises and extensive hands-on practice
A chatbot is a computerized assistant designed to automate user interactions on various messaging platforms, enabling tasks to be completed more efficiently without the need for direct human interaction.
In this instructor-led, live training, participants will gain an understanding of how to develop a chatbot as they work through the creation of sample chatbots using bot development tools and frameworks.
By the end of this training, participants will be able to:
Comprehend the diverse applications and uses of bots
Understand the entire process involved in developing bots
Explore the various tools and platforms utilized in building bots
Create a sample chatbot for Facebook Messenger
Develop a sample chatbot using Microsoft Bot Framework
Audience
Developers interested in creating their own bot for government use
Format of the Course
Part lecture, part discussion, exercises, and extensive hands-on practice
This instructor-led, live training in Austin (online or onsite) is aimed at engineers who wish to explore the applicability of artificial intelligence to mechatronic systems for government.
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 fundamental concepts of neural networks and various learning methods.
Select appropriate artificial intelligence approaches for addressing real-life problems in public sector environments.
Implement AI applications specifically tailored to mechatronic engineering projects for government use.
A Smart Robot is an Artificial Intelligence (AI) system capable of learning from its environment and experiences, enhancing its capabilities through acquired knowledge. These robots can collaborate with humans, working alongside them and adapting to their behavior. They are equipped to perform both manual labor and cognitive tasks. In addition to physical robots, Smart Robots can also exist as purely software-based systems, residing in a computer without any moving parts or physical interaction.
In this instructor-led, live training, participants will gain insights into various technologies, frameworks, and techniques for programming mechanical Smart Robots. They will then apply this knowledge to complete their own Smart Robot projects.
The course is structured into four sections, each consisting of three days of lectures, discussions, and hands-on robot development in a live lab environment. Each section concludes with a practical, hands-on project to enable participants to practice and demonstrate their acquired skills.
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 utilized for programming the robots.
By the end of this training, participants will be able to:
Understand key concepts used in robotic technologies
Manage the interaction between software and hardware in a robotic system
Implement the software components that underpin Smart Robots
Build and operate a simulated mechanical Smart Robot capable of seeing, sensing, processing, grasping, navigating, and interacting with humans through voice
Enhance 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 extensive hands-on practice
Note
To customize any aspect of this course (programming language, robot model, etc.), please contact us to arrange. This training is designed to meet the specific needs of professionals for government applications.
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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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