Master in Strategic Management of Humanoid Robotics and Physical AI

If you have no prior experience in robotics and artificial intelligence, you will have access to a leveling prework designed to give you the necessary foundations before starting the master. You will learn the key vocabulary and concepts through practical, dynamic and self-correcting content, with a final self-assessment quiz.

What a sensor, an actuator and a motor are, and how they differ. Types of robots: industrial, cobot, AMR/AGV, drone, robotic arm and humanoid What an operating system is Predictive AI: Machine Learning and Deep Learning

AI, generative AI and the evolution from assistants to AI agents Language models (LLM), multimodal models and diffusion models Computer vision and digital twin

Robotics is evolving from traditional automation toward robots capable of perceiving, interpreting and acting thanks to AI. This unit maps out the current landscape of the sector (humanoid, industrial, mobile and autonomous robots) and its business dimension: suppliers and manufacturers, price ranges, supply chains, frontier technologies and the public funding options available for a robotization project.

Automating vs. robotizing Generative AI and physical AI The emergence of humanoid robotics Impact on productivity and efficiency Software architecture behind the operation of a robot State of the art, industry trends and key companies

Landscape of suppliers and manufacturers, price ranges and business models Supply chains and availability Frontier technologies available on the market today Markets and sectors in early adoption

Students will analyze real and potential applications of robotics across different sectors. This unit connects the technology with concrete business problems, showing where a robot can add value and what conditions must be met for its deployment to make sense.

Applications of robotics in the B2B market Applications of robotics in B2C markets Robotics in healthcare and personal assistance

Robotics for security, inspection and critical operations New opportunities and trends in robotics

Students will learn the architecture of industrial robots and non-humanoid autonomous systems. The unit covers robotic arms, cobots, mobile robots, AGVs, AMRs, drones, inspection robots and other solutions already in use in production and logistics environments.

Components Robotic arms Collaborative robots Autonomous mobile robots

Guided and autonomous vehicles Drones Sector-specific specialized robots

Students will learn the main parts of a humanoid robot and how it captures information from its environment in order to act correctly. The unit examines the role of its body structure, joints, cameras, sensors, actuators, motors, batteries and processing units within an integrated robotic architecture.

Body structure of the humanoid Onboard computer Joints and mobility Arms, legs and wheels Hands and manipulation Cameras and vision

Tactile and depth sensors Force and pressure sensors Motors and actuators Batteries, battery life and cooling Connectivity

This unit introduces how the operating system and control software make it possible to coordinate sensors, actuators, processing, communication and task execution. No prior programming knowledge is required: the goal is to understand how the software that allows a robot to move, navigate, manipulate objects and carry out physical actions is organized.

The robot's operating system Control software The relationship between hardware and software Coordination of sensors and actuators

Communication between modules Movement and navigation Trajectories and displacement Object manipulation

This unit focuses directly on their application to robotics: how these models process language, images, video and context to help robots interpret instructions, understand environments and support decision-making in physical tasks.

AI models: VLA and WAM Interpreting instructions Understanding images and video

Context and basic reasoning Application in intelligent robots Current limits of these models

Computer vision allows robots to identify objects, people, obstacles, materials, signals and situations. This unit shows how AI models help the robot interpret images, classify elements and make decisions based on visual information, incorporating hands-on simulations (driven by the AI agents covered in unit 6) in which the robot must move, orient itself and avoid obstacles
in different environments.

Object recognition People detection Obstacle identification Interpreting spaces Cameras and visual sensors

Computer vision models Visual decision-making Simulation with obstacles LiDAR, radar and other sensors

AI agents can help configure environments, generate code, prepare simulators, analyze errors, document processes and create small robotic functions. This unit connects robotics with the agents and automation track, providing a differentiating approach.

Configuration and development of AI agents Agents for configuring simulators Orchestration of AI agents Assisted code generation Creating robotic functions

Interpreting errors Test automation Assisted technical documentation Integration with development environments

Robots need data to understand tasks, spaces, objects and processes. This unit explains what information can be used to train, contextualize or customize robots, and presents the main methods for adapting their functions: simulation, teleoperation, human demonstrations, first-person recordings, motion capture and emerging interfaces such as UMI.

Visual data and video Maps and environmental context Instructions and operational data Training in simulators

Robot teleoperation Learning from demonstration Motion capture and UMI Real vs. synthetic data

Companies can prepare for robotics before acquiring a robot. This topic teaches how to document processes, tasks, spaces, materials, exceptions, human decisions, and environmental conditions, with the goal of facilitating future robotic deployments, more precise training, and better adaptation of the robot to the real operating context.

Process mapping Recording of real tasks Workflow logging Identification of exceptions Mapping of spaces and routes

Obstacles and work zones Materials and environmental conditions Internal data preparation Hardware for data collection

This topic combines the use of advanced simulations and digital twins to validate robotic projects before physical deployment with the practical configuration of simulated scenarios: objects, obstacles, routes, work zones, people, and physical variables. Students will learn both to represent real environments (factories, warehouses, hospitals, production lines) and to adjust the variables that determine the robot's behavior within them, anticipating operational problems and reducing risks.

Advanced simulations and digital twins Representation of real environments Scenario design and configuration Objects, routes, and obstacles

Work zones and floor variables Friction, pressure, lighting, and visibility Unforeseen conditions Bottleneck detection and risk reduction

This topic addresses how to validate what has been simulated against the real world and what limits, deviations, or risks may arise when physically deploying a robot. It covers how to design pilot tests, verify hypotheses, detect perception or movement errors, and prepare protocols to reduce failures during deployment in real operations.

Simulation-reality gap Hypothesis validation Pilot tests Perception errors

Movement failures Unmodeled variables Operational risks Verification protocols

Before deploying robots, you have to identify which processes make sense. This topic teaches how to detect repetitive, hazardous, costly, manual, inefficient, or hard-to-staff tasks, and how to assess whether robotics can deliver real value, primarily through the representation of processes in flowcharts.

Representing processes in flowcharts Identifying inefficiencies Hazardous tasks High operating costs and labor shortages

Inefficient manual processes Automation opportunities Prioritizing use cases Technical and economic feasibility

In this topic, students will learn to design new optimized processes that integrate robotics and Artificial Intelligence and to redesign existing processes, selecting within the redesign exercise itself the most suitable type of robot (humanoid, robotic arm, cobot, AMR, AGV, drone, or another solution) according to the environment, the task, the cost, and the complexity of deployment. It also examines how the roles of the operators involved evolve.

Designing new processes Redesigning existing processes Robot selection criteria (humanoid vs. specialized) Deployment cost and complexity Level of autonomy and integration with existing systems

Task distribution and coordination among robots Human-robot operations and human supervision New operational roles Operational and collaborative safety Scalability of the solution and productivity measurement

Robotic deployment requires testing, calibration, supervision, and ongoing maintenance. This topic covers rollout phases, pilots, scaling, monitoring, incident management, preventive and corrective maintenance, software updates, and shutdown protocols.

Rollout phases Pilots and initial testing Robot calibration Operational monitoring

Preventive and corrective maintenance Incident management Shutdown and safety protocols Technology comparison systems: benchmarks

This topic brings together the management perspective of the master. Students will learn to design the most suitable business strategy for each case, lead and plan projects, estimate costs, calculate return on investment, and analyze the available funding options.

Business strategy Resource selection and sizing Cost analysis and return on investment Deployment budget Financial plan and funding models

Project planning and management Public funding: Kit Digital, Next Generation funds, CDTI and ICO lines. Criteria for evaluating and selecting vendors. Coordination of human-robot teams

Regulatory landscape applied to robotics and physical AI: how it is regulated in Spain and the European Union, and how this compares with the regulatory frameworks of other regions, including their impact on the pace of business adoption.

Spanish regulatory framework (machinery safety regulations, AI supervision agency) European AI Regulation (AI Act) Data protection and the GDPR applied to robots

Comparison with the U.S. and Asia International robotic safety standards Implications for the pace of business adoption

This topic analyzes the technological risks associated with using connected robots and autonomous systems: from the security of sensors and cameras to protection against unauthorized remote access or the manipulation of autonomous decisions.

Key principles of cybersecurity Privacy and data protection Risks in sensors, cameras, and connectivity Safety in autonomous decisions

Secure integration with enterprise systems Remote access and secure teleoperation Cybersecurity in connected robots

This topic addresses the ethical and labor implications of introducing intelligent robots into the company: liability for errors, algorithmic transparency, and how to manage the impact on teams and their roles, with specific emphasis on the relationship with worker representatives (comités de empresa and unions) in robotization processes.

Ethics and algorithmic accountability Transparency and explainability Social and labor impact of automation Legal framework for consulting worker representatives (comités de empresa, delegados sindicales; art. 64 del Estatuto de los Trabajadores)

Disclosure obligations regarding algorithmic systems that affect working conditions (the line opened by the "Ley Rider") Collective bargaining and robotization plans: what must be disclosed and what must be negotiated Change management and communication with teams New roles and reskilling

This unit analyzes the opportunities opened up by humanoid and industrial robotics from a professional, business, and investment perspective. The
student will learn about the new profiles in demand, potential entrepreneurial paths, emerging business models, and basic criteria for identifying investment opportunities or launching projects in the AI-driven robotics ecosystem.

New professional profiles Robotic integration consulting Entrepreneurship in robotics and AI Emerging business models Startups and companies in the sector

Robotics as an investment opportunity Company analysis, valuation, and investment Market trends The future of work in humanoid robotics