AI for Robotics Training in Varna

This practical, live-led training in Varna explores the art of rapid prototyping in robotics using ROS 2 and Docker. Learners will acquire hands-on expertise in constructing, testing, and deploying modular robotic systems, while gaining key skills in containerization, simulation, and version control to support scalable development efforts.

This practical HRI course in Varna explores voice, gesture, and collaborative control. Participants will learn to craft intuitive, multimodal interfaces that facilitate safe human-robot collaboration, with a strong emphasis on applied design principles, speech recognition, gesture detection, and shared autonomy to optimize both usability and safety.

This practical training module on Varna delves into ROS-PLC integration and digital twins for industrial automation. Participants will learn to synchronize robotic systems with PLCs, construct digital replicas for simulation, and enhance production workflows through real-time data exchange and sophisticated monitoring methods.

Join this advanced training in Varna to master deep learning applications in robotic manipulation. Gain expertise in perception, grasp planning, and control by utilizing neural networks and ROS 2. Through hands-on simulation exercises, you will be prepared to effectively deploy intelligent and adaptable grasping solutions on physical robotic arms.

An advanced course on Varna focusing on the design and coordination of multi-robot systems. Participants will learn to model swarm intelligence, execute distributed decision-making, and optimize agent collaboration through hands-on simulations in ROS 2 and Gazebo, aimed at real-world applications.

This instructor-led, live training in Varna guides advanced professionals in integrating TinyML into autonomous robotics. Participants will learn to design optimized models, implement on-device perception pipelines, and deploy lightweight AI on embedded hardware to achieve real-time autonomy.

This 21-hour, instructor-led training in Varna addresses safety cases, verification, and explainable AI in robotics. It enables you to apply hazard analysis, ethical governance, and compliance standards to ensure transparent and trustworthy robotic decision-making in real-world scenarios.

This live training in Varna guides intermediate engineers in deploying TinyML models for robotics. Learn to optimize inference for speed and energy, integrate AI into control systems, and build autonomous, low-latency robotic solutions directly on embedded hardware.

This instructor-led, live training in Varna (online or onsite) targets intermediate-level professionals interested in exploring the role of collaborative robots (cobots) and other human-centric AI systems in modern workplaces.

By the conclusion 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.

This instructor-led live training in Varna delves into the principles of reinforcement learning and deep RL techniques such as PPO and DDPG. Participants will implement algorithms using Python and PyTorch, integrate them with ROS 2 and OpenAI Gym, and train autonomous robots to resolve complex tasks.

This instructor-led training in Varna focuses on integrating computer vision into robotics through the use of OpenCV and deep learning technologies. Attendees will gain the skills necessary to construct perception pipelines, merge advanced detection models, and implement robust systems on embedded platforms to facilitate autonomous control.

This instructor-led, live training in Varna (online or onsite) is designed for advanced robotics engineers and AI researchers aiming to utilize Multimodal AI. The objective is to integrate various sensory inputs to develop highly autonomous and efficient robots capable of seeing, hearing, and touching.

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

• Implement multimodal sensing in robotic systems.
• Develop AI algorithms for sensor fusion and decision-making.
• Create robots that can perform complex tasks in dynamic environments.
• Address challenges in real-time data processing and actuation.

In Varna, this live training empowers advanced engineers with specialized AI competencies in robotic perception, strategic planning, and control mechanisms. Learners will gain the ability to utilize sensor fusion, construct motion algorithms, and implement learning-based strategies to effectively embed intelligent systems into smart manufacturing operations.

Designed for intermediate robotics engineers, this instructor-led live training in Varna provides hands-on expertise in autonomous navigation and SLAM using ROS 2. Participants will learn to integrate LiDAR sensors, develop mapping algorithms, simulate scenarios in Gazebo, and deploy practical navigation stacks on physical robots.

This live, instructor-led session in Varna (online or onsite) targets intermediate-level professionals aiming to elevate their capabilities in designing, coding, and deploying intelligent robotic systems for automation and broader use cases.

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

• Comprehend the principles of Physical AI and its role in robotics and automation.
• Design and program intelligent robotic systems for dynamic environments.
• Implement AI models to facilitate autonomous decision-making in robots.
• Employ simulation tools for rigorous robotic testing and optimization.
• Address technical challenges including sensor fusion, real-time processing, and energy efficiency.

This instructor-led live training in Varna (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 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.