Artificial Intelligence for Robotics

Course Description

Artificial intelligence is transforming robotics, enabling machines to perceive their surroundings, understand context, make decisions, and operate in complex real-world environments. This course introduces the AI techniques and system concepts behind modern intelligent robots, with applications across logistics, manufacturing, healthcare, autonomous navigation, service robotics, and emerging physical AI systems.

Students will study the core AI building blocks used in robotics, including perception, sensor fusion, mapping, object detection, segmentation, natural language interaction, reasoning, and learning-based decision-making. The course connects these techniques to practical robotics architectures and industry use cases, highlighting what it takes to move from a model or prototype to a deployed robotic system.

Students will gain hands-on exposure to model training and evaluation workflows while developing a systems-level understanding of how AI, software, sensors, and robotic behavior come together. 

Learning Outcomes  
Through discussion, technical demonstration and hands-on practice, you will learn to

• Design, develop, and deploy AI-driven applications for real-world robotic systems.
• Translate real-world use cases into effective AI-powered robotics solutions across various industries such as logistics, manufacturing, and healthcare.  
• Apply machine learning techniques such as computer vision, sensor fusion, and decision-making models to enable robotic perception, control and autonomy.

Topics Include

• AI and robotics industry landscape
• Robotics system architecture
• Perception systems: cameras, sensors, and sensor fusion
• Object detection, classification, and segmentation
• SLAM, mapping, navigation, and manipulation
• Natural language interfaces for robots
• Multimodal AI and foundation models for robotics
• Vision-language-action models and emerging physical AI systems
• Data collection, model training, validation, and evaluation
• Case studies, demonstrations, and project presentations
• Demonstrations and project presentations 

Skills Needed

Students should be proficient in programming languages, such as C++ or Python. Familiarity with machine learning concepts, AI/ML frameworks, linear algebra, and higher-level mathematics is recommended.

Additional Information

AI* - This course introduces the AI foundations of physical intelligence: how robots use perception, language, learning, and reasoning to operate in complex real-world environments using methods such as computer vision, deep learning, and multimodal foundation models.