|SCIENTIFIC DISCIPLINARY SECTOR||ING-INF/05|
Artificial Intelligence for Robotics II (AI4Ro2) describes advanced concepts. methods and techniques at the intersection between Artificial Intelligence (AI) and Robotics practice. The aim of the course is to provide future scholars and professionals with conceptual tools and practical knowledge about how to integrated cutting-edge AI techniques in robot software architectures, and to guide future engineers about how to do so. The course on real-world, advanced scenarios, e.g., autonomous vehicles, collaborative robots, assistive robots.
Artificial Intelligence for Robotics 2 is the logic follow-up of Artificial Intelligence for Robotics 1. In this course, the students will be introduced to concepts related to knowledge representation and reasoning (ontologies, description logics, OWL, subsumption, instance checking), planning for hybrid domains (with a particular focus on discrete/continuous domains), as well as AI-based robot motion algorithms (es., RRTs, probabilistic roadmaps, belief-space planning).
The main objective of Artificial Intelligence for Robotics II (AI4Ro2) is to provide students and scholars with methodological approaches and pragmatic knowledge about how to integrate advanced AI techniques in robot architectures to make them capable of operating in real-world environments in a robust way.
As robots are deployed in scenarios progressively more unstructured, and in conditions whereby their behaviour cannot be easily foreseen in advance, the degree of intelligence they must be provided with becomes of critical importance. Encoding advanced AI algorithms in robots operating in such conditions requires a careful trade-off between their capabilities and the associated computational requirements. The problem of defining an AI-based robot architecture requires:
AI4Ro2 will provide a reasoned treatment of current, state‐of‐the‐art AI-backed perception, cognition, knowledge representation, reasoning, and action approaches, as well as a critical discussion about typical scenarios, use cases, and solutions.
AI4Ro2 is organized in four key topics. Each topic will be taught via theoretical concepts followed by practical work. AI4Ro2 will use the flipped classroom teaching methodology. Students are strongly encouraged to propose novel solutions to specific practical problems, which originate from real-world research challenges or industrial needs.
AI4Ro2 is a new, highly experimental course. AI4Ro2 mixes up theoretical insights about AI techniques with practical knowledge about how to make them work in robots. AI4Ro2 is organised around the following topics:
TOPIC 1: Introduction and motivations:
TOPIC 2: Knowledge representation and reasoning:
Practice classes focused on TOPIC 2.
TOPIC 3: Planning in discrete/continuous domains:
Practice classes focused on TOPIC 3.
TOPIC 4: AI-based robot perception and motion algorithms:
Practice classes focused on TOPIC 4.
Relevant material will be given by the teachers and the instructors during the lectures.
FULVIO MASTROGIOVANNI (President)
MAURO VALLATI (President Substitute)
All class schedules are posted on the EasyAcademy portal.
The AI4Ro2 final mark is based on assignments (50%) and on a written exam (50%). Assignments work as follows:
Please note that:
Properly carrying out an assignment means providing:
The written exam will check single students' knowledge about the topics discussed throughout the course.
The overall grade will be determined on the basis of: