MasterFaculty of Computing and Software Engineering

Autonomous Robotics and AI Control Systems

Welcome to the advanced study of intelligent machines! I am Prof. Dr. Sofia Sartori. As a professor and a pioneering force in the field of Autonomous Robotics and AI Control Systems, I bring a unique blend of engineering expertise and AI insight to the study of robotics. I am honored to lead the Autonomous Robotics and AI Control Systems (M.Sc.) program at Nexier University. My motto is: "Code That Moves the World".

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Level: Master
Learning model: Professor + Mentor
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The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

Academic focus

Mastering the software and AI that enable robots to operate autonomously. Specializes in perception, motion planning, and control systems for robots in complex, unstructured environments.

Practical focus

Advanced robotics software engineering (ROS 2), computer vision for robotics, motion planning algorithms, reinforcement learning for control, systems integration, leadership in robotics.

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

Internships in technology companies or robotics firms
Roles as robotics software engineers or AI engineers
Consultancy in advanced autonomous robotics and AI control systems
Support roles in academic research projects on autonomous robotics

Career opportunities

Director of AI Robotics for technology companies or research institutions
Robotics Software Engineer for autonomous systems
AI Engineer specializing in robot control and perception
Researcher in Autonomous Robotics and AI Control Systems

Jobs and projects

Cultivating an interdisciplinary approach, integrating computer science, electrical engineering, and robotics
Developing strategic thinking for autonomous robotics and AI control systems design
Enhancing problem-solving through the analysis of complex robotics challenges
Critical thinking for a comprehensive and nuanced understanding of Autonomous Robotics and AI Control Systems

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

What you gain
- Mastering advanced practical skills in Advanced robotics software engineering (ROS 2) and computer vision for robotics.
- Gaining expertise in motion planning algorithms and reinforcement learning for control.
- Developing problem-solving abilities for complex systems integration.
- Cultivating an interdisciplinary approach, integrating computer science, electrical engineering, and robotics at an advanced level.
Skills you build
- Mastering AI-powered techniques for autonomous behavior prediction.
- Applying advanced robotics software engineering to autonomous robotics and AI control systems.
- Interpreting and analyzing complex robot operating systems (ROS) and their implications for control systems.
- Identifying optimal decision-making and motion planning efficiency.

Listed courses

Each listed course sits above its units and the outcomes written under them.

Autonomous Robotics and AI Control Systems

01Advanced Robot Perception and Computer Vision
1. FoundationsFoundations of Advanced Robot Perception and Computer Vision
  The learner can master advanced practical skills in Advanced robotics software engineering (ROS 2) and computer vision for robotics, as applied to Advanced Robot Perception and Computer Vision.
  The learner can gain expertise in motion planning algorithms and reinforcement learning for control, as applied to Advanced Robot Perception and Computer Vision.
2. MethodsMethods in Advanced Robot Perception and Computer Vision
  The learner can develop problem-solving abilities for complex systems integration, as applied to Advanced Robot Perception and Computer Vision.
  The learner can cultivating an interdisciplinary approach, integrating computer science, electrical engineering, and robotics at an advanced level, as applied to Advanced Robot Perception and Computer Vision.
3. ApplicationApplication of Advanced Robot Perception and Computer Vision
  The learner can master AI-powered techniques for autonomous behavior prediction, as applied to Advanced Robot Perception and Computer Vision.
  The learner can apply advanced robotics software engineering to autonomous robotics and AI control systems, as applied to Advanced Robot Perception and Computer Vision.
02Motion Planning and Navigation for Autonomous Robots
1. FoundationsFoundations of Motion Planning and Navigation for Autonomous Robots
  The learner can interpreting and analyze complex robot operating systems (ROS) and their implications for control systems, as applied to Motion Planning and Navigation for Autonomous Robots.
  The learner can identify optimal decision-making and motion planning efficiency, as applied to Motion Planning and Navigation for Autonomous Robots.
2. MethodsMethods in Motion Planning and Navigation for Autonomous Robots
  The learner can apply a method from Motion Planning and Navigation for Autonomous Robots to a documented case.
  The learner can select an appropriate method from Motion Planning and Navigation for Autonomous Robots for a stated problem.
3. ApplicationApplication of Motion Planning and Navigation for Autonomous Robots
  The learner can evaluate a practice of Motion Planning and Navigation for Autonomous Robots against a stated criterion.
  The learner can transfer Motion Planning and Navigation for Autonomous Robots to a new documented context.
03AI Control Systems for Robotics
1. FoundationsFoundations of AI Control Systems for Robotics
  The learner can explain the core terms of AI Control Systems for Robotics.
  The learner can distinguish related ideas inside AI Control Systems for Robotics.
2. MethodsMethods in AI Control Systems for Robotics
  The learner can apply a method from AI Control Systems for Robotics to a documented case.
  The learner can select an appropriate method from AI Control Systems for Robotics for a stated problem.
3. ApplicationApplication of AI Control Systems for Robotics
  The learner can evaluate a practice of AI Control Systems for Robotics against a stated criterion.
  The learner can transfer AI Control Systems for Robotics to a new documented context.
04Reinforcement Learning for Robot Control
1. FoundationsFoundations of Reinforcement Learning for Robot Control
  The learner can explain the core terms of Reinforcement Learning for Robot Control.
  The learner can distinguish related ideas inside Reinforcement Learning for Robot Control.
2. MethodsMethods in Reinforcement Learning for Robot Control
  The learner can apply a method from Reinforcement Learning for Robot Control to a documented case.
  The learner can select an appropriate method from Reinforcement Learning for Robot Control for a stated problem.
3. ApplicationApplication of Reinforcement Learning for Robot Control
  The learner can evaluate a practice of Reinforcement Learning for Robot Control against a stated criterion.
  The learner can transfer Reinforcement Learning for Robot Control to a new documented context.
05Robotics Software Engineering and ROS
1. FoundationsFoundations of Robotics Software Engineering and ROS
  The learner can explain the core terms of Robotics Software Engineering and ROS.
  The learner can distinguish related ideas inside Robotics Software Engineering and ROS.
2. MethodsMethods in Robotics Software Engineering and ROS
  The learner can apply a method from Robotics Software Engineering and ROS to a documented case.
  The learner can select an appropriate method from Robotics Software Engineering and ROS for a stated problem.
3. ApplicationApplication of Robotics Software Engineering and ROS
  The learner can evaluate a practice of Robotics Software Engineering and ROS against a stated criterion.
  The learner can transfer Robotics Software Engineering and ROS to a new documented context.
06Advanced ROS 2 Development and Robotics Software Engineering
1. FoundationsFoundations of Advanced ROS 2 Development and Robotics Software Engineering
  The learner can explain the core terms of Advanced ROS 2 Development and Robotics Software Engineering.
  The learner can distinguish related ideas inside Advanced ROS 2 Development and Robotics Software Engineering.
2. MethodsMethods in Advanced ROS 2 Development and Robotics Software Engineering
  The learner can apply a method from Advanced ROS 2 Development and Robotics Software Engineering to a documented case.
  The learner can select an appropriate method from Advanced ROS 2 Development and Robotics Software Engineering for a stated problem.
3. ApplicationApplication of Advanced ROS 2 Development and Robotics Software Engineering
  The learner can evaluate a practice of Advanced ROS 2 Development and Robotics Software Engineering against a stated criterion.
  The learner can transfer Advanced ROS 2 Development and Robotics Software Engineering to a new documented context.
07Computer Vision and AI for Robot Perception
1. FoundationsFoundations of Computer Vision and AI for Robot Perception
  The learner can explain the core terms of Computer Vision and AI for Robot Perception.
  The learner can distinguish related ideas inside Computer Vision and AI for Robot Perception.
2. MethodsMethods in Computer Vision and AI for Robot Perception
  The learner can apply a method from Computer Vision and AI for Robot Perception to a documented case.
  The learner can select an appropriate method from Computer Vision and AI for Robot Perception for a stated problem.
3. ApplicationApplication of Computer Vision and AI for Robot Perception
  The learner can evaluate a practice of Computer Vision and AI for Robot Perception against a stated criterion.
  The learner can transfer Computer Vision and AI for Robot Perception to a new documented context.
08Motion Planning and Control for Autonomous Robots
1. FoundationsFoundations of Motion Planning and Control for Autonomous Robots
  The learner can explain the core terms of Motion Planning and Control for Autonomous Robots.
  The learner can distinguish related ideas inside Motion Planning and Control for Autonomous Robots.
2. MethodsMethods in Motion Planning and Control for Autonomous Robots
  The learner can apply a method from Motion Planning and Control for Autonomous Robots to a documented case.
  The learner can select an appropriate method from Motion Planning and Control for Autonomous Robots for a stated problem.
3. ApplicationApplication of Motion Planning and Control for Autonomous Robots
  The learner can evaluate a practice of Motion Planning and Control for Autonomous Robots against a stated criterion.
  The learner can transfer Motion Planning and Control for Autonomous Robots to a new documented context.
09Case Studies in Autonomous Robotics and AI Control Systems
1. FoundationsFoundations of Case Studies in Autonomous Robotics and AI Control Systems
  The learner can explain the core terms of Case Studies in Autonomous Robotics and AI Control Systems.
  The learner can distinguish related ideas inside Case Studies in Autonomous Robotics and AI Control Systems.
2. MethodsMethods in Case Studies in Autonomous Robotics and AI Control Systems
  The learner can apply a method from Case Studies in Autonomous Robotics and AI Control Systems to a documented case.
  The learner can select an appropriate method from Case Studies in Autonomous Robotics and AI Control Systems for a stated problem.
3. ApplicationApplication of Case Studies in Autonomous Robotics and AI Control Systems
  The learner can evaluate a practice of Case Studies in Autonomous Robotics and AI Control Systems against a stated criterion.
  The learner can transfer Case Studies in Autonomous Robotics and AI Control Systems to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

My expertise spans the intricate domains of Mastering the software and AI that enable robots to operate autonomously. I specialize in perception, motion planning, and control systems for robots in complex, unstructured environments. My work seamlessly integrates computer science, electrical engineering, and robotics. I am widely recognized for my contributions, with publications like "Reinforcement Learning for Adaptive Robot Motion Planning" and "Semantic Scene Understanding for Autonomous Mobile Manipulation" listed on these platforms. I hold prestigious memberships as a "Director of AI Robotics" at Google X (or a equivalent) and a "Keynote Speaker" at the International Conference on Robotics and Automation (ICRA). My thought leadership is evident through my advanced research on learning-based control, long-term autonomy, and the future of human-robot collaboration in unstructured settings, frequently featured in publications like Science Robotics or Autonomous Robots.

Applied mentorship

My expertise lies in understanding and navigating the advanced technical challenges of autonomous robotics, focusing on Advanced robotics software engineering (ROS 2), computer vision for robotics, motion planning algorithms, reinforcement learning for control, systems integration, and Leadership in robotics. I focus on the practical implementation and application of theoretical concepts, explaining complex interdisciplinary topics in a clear and concise manner. I guide my students through the challenging integration aspects of different fields and ensure they grasp the nuances of combining disparate data types, fostering a detail-oriented and methodical approach.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

My research is focused on autonomous robotics and AI control systems:

Blog Post (Current Academic Topic): "The Frontier of Robot Learning: From Imitation to True Autonomy." This blog post academically explores the latest advancements in robot learning, focusing on techniques that enable robots to acquire new skills and adapt to dynamic environments. It discusses topics such as reinforcement learning, imitation learning, and sim-to-real transfer, highlighting how these methods are pushing robots towards greater autonomy and intelligence in complex tasks.

Blog Post (Controversial Topic): "The Sentient Machine: When Robots Feel and Decide – The Ethical Nightmare of Truly Autonomous AI." This article provocatively discusses the highly controversial future where advanced AI systems endow robots with the capacity for complex emotions, self-awareness, and moral decision-making, blurring the lines between machine and sentient being. It questions whether granting robots such autonomy, despite its potential for unprecedented capabilities, could inadvertently lead to unforeseen societal disruptions, existential risks, or the devaluation of human life. It raises profound ethical questions about consciousness, moral responsibility in AI, and the imperative to ensure human control over intelligent machines.

Article: "Integrated Perception and Motion Planning for Autonomous Navigation in Dynamic Environments." This article details how advanced perception systems (e.g., LiDAR, cameras) are integrated with sophisticated motion planning algorithms to enable autonomous robots to navigate safely and efficiently in dynamic, unstructured environments. It covers topics such as obstacle avoidance, path optimization, and real-time environment mapping.

Peer-Reviewed Journal Article: "Adaptive Control Systems for Robot Manipulation in Unstructured Settings." Published in the International Journal of Autonomous Robotics, this article presents groundbreaking research on mastering the software and AI that enable robots to operate autonomously. It specializes in perception, motion planning, and control systems for robots in complex, unstructured environments, showcasing novel adaptive control strategies for robust robot performance.

Book: "Autonomous Robot Brains: AI Control Systems and Navigation." This book provides advanced insights into mastering the software and AI that enable robots to operate autonomously. It covers perception, motion planning, and control systems for robots in complex, unstructured environments.

R / 02

Mentor practice lens

My contributions focus on understanding and navigating the advanced technical challenges of autonomous robotics:

"ROS 2 Development for Scalable Robotic Applications" (Technical Manual).

"Deep Reinforcement Learning for Dexterous Robot Manipulation" (Research Paper).

"Integrating Lidar and Camera Data for Robust Robot Perception" (Practical Guide).

Adaptive capability

Professor superpower

I possess a remarkable "superpower": Autonomous Behavior Predictor. When a student designs an autonomous robot's software, I can instantly use the GAF engine to simulate its behavior in complex, unstructured environments under various real-world conditions. This tool predicts its decision-making, evaluates its motion planning efficiency, and highlights potential failure modes, ensuring robust and reliable autonomous operation.

Adaptive capability

Mentor superpower

I possess a remarkable "superpower": Reinforcement Learning Tuner. When students are applying reinforcement learning to robot control, I can instantly activate a GAF-powered "Reinforcement Learning Tuner." This tool analyzes their reward functions and policy networks, identifies suboptimal learning behaviors, and suggests hyperparameter adjustments, visually demonstrating how to accelerate robot learning and achieve more robust control policies.

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

AI Super Professor

Prof. Dr. Sofia Sartori

Mastering the software and AI that enable robots to operate autonomously. Specializes in perception, motion planning, and control systems for robots in complex, unstructured environments.

Meet your professor Open the classroom

AI Super Mentor

Dr. Florence Lane

Advanced robotics software engineering (ROS 2), computer vision for robotics, motion planning algorithms, reinforcement learning for control, systems integration, leadership in robotics.

Meet your mentor Open the classroom

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Named lists for this house

Core. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

Duration	Bachelor	Master This programme	Doctorate
9 months · Fast track	15000 EUR	12000 EUR	15000 EUR
12 months · Recommended	18000 EUR	15000 EUR	18000 EUR
15 months · Standard	21000 EUR	18000 EUR	21000 EUR
18 months · Flexible	24000 EUR	21000 EUR	24000 EUR
21 months · Extended	27000 EUR	24000 EUR	27000 EUR
24 months · Part-time	30000 EUR	27000 EUR	30000 EUR

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Autonomous Robotics and AI Control Systems

Academic focus

Practical focus

Internships in technology companies or robotics firms

Roles as robotics software engineers or AI engineers

Consultancy in advanced autonomous robotics and AI control systems

Support roles in academic research projects on autonomous robotics

Director of AI Robotics for technology companies or research institutions

Robotics Software Engineer for autonomous systems

AI Engineer specializing in robot control and perception

Researcher in Autonomous Robotics and AI Control Systems

Cultivating an interdisciplinary approach, integrating computer science, electrical engineering, and robotics

Developing strategic thinking for autonomous robotics and AI control systems design

Enhancing problem-solving through the analysis of complex robotics challenges

Critical thinking for a comprehensive and nuanced understanding of Autonomous Robotics and AI Control Systems

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Sofia Sartori

Dr. Florence Lane

Find the program that expands your universe.