AI for Robotics Training in Copenhagen

This practical, instructor-led training in Copenhagen focuses on rapid prototyping for robotics using ROS 2 and Docker. Participants will acquire the skills to build, test, and deploy modular robotic systems efficiently, gaining hands-on experience in containerization, simulation, and version control for scalable development.

This hands-on HRI course in Copenhagen focuses on voice, gesture, and collaborative control. Discover how to build intuitive, multimodal interfaces for safe human-robot collaboration, with an emphasis on practical design principles, speech recognition, gesture detection, and shared autonomy to boost usability and safety.

This practical training in Copenhagen explores ROS-PLC integration and digital twins within industrial automation. It enables you to synchronize robotic systems with PLCs, build digital replicas for simulation purposes, and enhance production workflows via real-time data exchange and sophisticated monitoring methods.

Advanced training in Copenhagen focusing on deep learning for robotic manipulation. Master perception, grasp planning, and control through neural networks and ROS 2. Practical simulation exercises prepare you to effectively deploy intelligent and adaptable grasping solutions on real robotic arms.

An advanced training in Copenhagen focusing on the design and coordination of multi-robot systems. Participants will learn to model swarm intelligence, implement distributed decision-making, and optimise collaboration among agents through hands-on simulation in ROS 2 and Gazebo for practical real-world applications.

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

This 21-hour instructor-led training on Copenhagen delves into safety cases, verification, and explainable AI for robotics. You will learn to apply hazard analysis, ethical governance, and compliance standards to guarantee transparent and trustworthy robotic decision-making in real-world applications.

This live training in Copenhagen supports intermediate engineers in deploying TinyML models for robotics. Gain the skills to optimize inference for speed and energy efficiency, integrate AI into control systems, and create autonomous, low-latency robotic solutions directly on embedded hardware.

This interactive, instructor-led training in Copenhagen, available either online or onsite, is tailored for intermediate-level professionals looking to explore the impact of collaborative robots (cobots) and other human-centric AI frameworks within contemporary business environments.

Upon completion of this program, participants will be equipped to:

• Grasp the fundamental principles of Human-Centric Physical AI and its practical applications.
• Investigate how collaborative robots contribute to elevating workplace efficiency.
• Recognize and mitigate common challenges arising from human-machine interactions.
• Craft workflows that maximize the collaborative potential between human teams and AI-driven systems.
• Foster an organizational culture that embraces innovation and adaptability in AI-enhanced settings.

This instructor-led live training in Copenhagen delves into the principles of reinforcement learning and advanced deep RL techniques such as PPO and DDPG. Participants will gain hands-on experience implementing algorithms with Python and PyTorch, integrating them with ROS 2 and OpenAI Gym, and training autonomous robots to tackle complex challenges.

This instructor-led training in Copenhagen focuses on applying computer vision to robotics through OpenCV and deep learning. Participants will master the implementation of perception pipelines, the integration of detection models, and the deployment of systems on embedded platforms for autonomous control.

This live, instructor-led course offered in Copenhagen (online or onsite) targets advanced robotics engineers and AI researchers. It focuses on utilizing Multimodal AI to fuse various sensory data streams, enabling the creation of highly autonomous and efficient robots that can see, hear, and touch.

By the conclusion of this training, participants will have the ability to:

• Integrate multimodal sensing into robotic systems.
• Design AI algorithms for effective sensor fusion and decision-making processes.
• Construct robots capable of managing complex tasks in changing environments.
• Overcome challenges associated with real-time data handling and actuation.

This instructor-led live training in Copenhagen provides intermediate robotics engineers with practical skills in autonomous navigation and SLAM using ROS 2. Participants will learn to integrate LiDAR sensors, apply mapping algorithms, run simulations in Gazebo, and deploy navigation stacks on real-world physical robots.

This instructor-led, live training in Copenhagen—delivered online or onsite—targets intermediate-level participants looking to elevate their skills in designing, programming, and deploying intelligent robotic systems for automation and broader uses.

By the end of this 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 drive autonomous decision-making in robots.
• Leverage simulation tools for effective robotic testing and optimization.
• Address key challenges including sensor fusion, real-time processing, and energy efficiency.

Aimed at engineers looking to understand the application of artificial intelligence in mechatronic systems, this instructor-led live training is delivered either online or onsite in Copenhagen.

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

• Gain a broad perspective on artificial intelligence, machine learning, and computational intelligence.
• Comprehend the principles behind neural networks and diverse learning techniques.
• Effectively select AI approaches for practical problem-solving.
• Develop AI applications for use in mechatronic engineering.