91411 - Intelligent Robotic Systems

Course Unit Page

  • Teacher Andrea Roli

  • Credits 6

  • SSD ING-INF/05

  • Teaching Mode Traditional lectures

  • Language English

  • Campus of Cesena

  • Degree Programme Second cycle degree programme (LM) in Computer Science and Engineering (cod. 8614)

  • Teaching resources on Virtuale

Academic Year 2021/2022

Learning outcomes

At the end of the course, students have acquired knowledge and competencies for designing a system composed of one or more robots (here, we call "robot" an autonomous system which exists in the physical world, can sense its environment and can act on it to achieve some goals). In particular, students know the main models, methods, architectures and tools for programming robots equipped with nontrivial computational and cognitive capabilities.

Course contents

Introduction to robotics

  • Notions of robot and its behavior in a physical environment
  • Main issues in intelligent robotic systems design
  • Complex dynamical systems: basic definitions and concepts propaedeutic to robotic topics (notions of phase and state space, trajectory, attractor, bifurcation, phase transition, chaos)

Main methods and approaches for programming robotic systems. Part 1: Deliberative control

  • Informed search algorithms. A* and its variants for path planning problems
  • Robot planning: main definitions and principles
  • STRIPS and its extensions
  • Nonlinear planning
  • Navigation problems and main solution approaches


Main methods and approaches for programming robotic systems. Part 2: Behavior-based robotics

  • The Subsumption Architecture
  • Artificial Evolution and Artificial Life
  • Automatic design of robot programs
  • Further discussions on embodiment: sensory-motor coordination, niches, morphological computation
  • Fuzzy logic and fuzzy systems
  • Behavior trees
  • Experimental evaluation and parameter tuning of control software for robots: practical guidelines


Robot learning

  • Associative learning
  • Reinforcement learning
  • Value-based learning and intrinsic motivation

Lab activities
Experiments with robotic simulators with the aim of experiencing with the various kinds of control and testing the knowledge acquired.


Course textbooks:

  • S. Nolfi, "Behavioral and Cognitive Robotics - An adaptive perspective", CNR-ISTC, 2021. Available for download: https://bacrobotics.com/
  • R. Pfeifer and C. Scheier, "Understanding intelligence", The MIT Press, 1999.

Additional books:

  • R. Pfeifer and J. Bongard, "How the body shapes the way we think", The MIT Press, 2007.
  • M. Mataric, "The Robotic Primer", The MIT Press, 2007.
  • U. Nehmzow, Robot Behaviour: Design, Description, Analysis and Modelling, Springer, 2009.


Papers and further teaching material are available from the course website.

Teaching methods

The course is composed of class lessons and lab activities. The latter activities will be organised such that students will have the opportunity of tackling the main issues in devising control programs for robots and making experience of the knowledge acquired during the lessons in the class. Whenever possible, students will be invited to work in teams. In addition, projects in the lab will be planned so as to enable students to integrate also their studies made attending related courses.

Assessment methods

The final exam aims at evaluating the extent to which students have reached the teaching objectives, i.e., the knowledge of the subjects taught in the course and the capability of designing and programming a robotic system.

The exam consists in the presentation and the discussion of a project developed by the students, possibly in small groups, and the discussion of topics illustrated in the course. Both activities contribute to 50% of the overall final mark and have to be done in the same oral exam.

The main criteria are the assessment of the knowledge acquired, the methodology used in the project and the scientific method used.

Besides the usual six exam dates per year, students can take the exam also by advance arrangement with the teacher.

Teaching tools

Students will be given teaching material prepared by the teacher (slides and papers published in journals and conferences). In addition, lab activities will make use of robotic simulators (e.g., ARGoS) and, whenever it is possible, of real robots.

The course textbooks and the teaching material provided cover all the topics presented in the course.

Office hours

See the website of Andrea Roli