Dario Floreano

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Full Professor

dario.floreano@epfl.ch +41 21 693 52 30 https://www.epfl.ch/labs/lis

EPFL STI IMT LIS
MED 1 1026 (Bâtiment MED)
Station 9
CH-1015 Lausanne

+41 21 693 52 30
+41 21 693 59 66
Office: MED 1 1026
EPFL > STI > IGM > LIS

Web site: Web site: https://lis.epfl.ch/

+41 21 693 52 30
EPFL > STI > PRN-ROBOTICS > PRN-ROBOT-GE

Web site: Web site: https://www.nccr-robotics.ch/

+41 21 693 52 30
EPFL > STI > STI-SMT > SMT-ENS

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Administrative data

Fields of expertise

Flying Robots

Evolutionary Robots

Soft Robots
Wearable Robots

Bio-inspired Robots

Bio-inspired Artificial Intelligence

Biography

Prof. Dario Floreano is director of the Laboratory of Intelligent Systems at the Swiss Federal Institute of Technology Lausanne (EPFL). Since 2010, he is the founding director of the Swiss National Center of Competence in Robotics, a research program that brings together more than 20 labs across Switzerland.

Prof. Floreano holds an M.A. in Vision, an M.S. in Neural Computation, and a PhD in Robotics. He has held research positions at Sony Computer Science Laboratory, at Caltech/JPL, and at Harvard University. His main research interests are Robotics and A.I. at the convergence of biology and engineering. Prof. Floreano made pioneering contributions to the fields of evolutionary robotics, aerial robotics, and soft robotics. He served in numerous advisory boards and committees, including the Future and Emerging Technologies division of the European Commission, the World Economic Forum Agenda Council, the International Society of Artificial Life, the International Neural Network Society, and in the editorial committee of several scientific journals. In addition, he helped spinning off two drone companies (senseFly.com and Flyability.com) and a non-for-profit portal on robotics and A.I. (RoboHub.org).

Books

Manuale sulle Reti Neurali, il Mulino (in Italian), 1996 (first edition), 2006 (second edition)
Evolutionary Robotics, MIT Press, 2000

Bio-Inspired Artificial Intelligence, MIT Press, 2008

Flying Insects and Robots, Springer Verlag, 2010

Teaching & PhD

Teaching

Microengineering

Mechanical Engineering

PhD Programs

Doctoral Program in Electrical Engineering

Doctoral Program in Microsystems and Microelectronics

Doctoral program in robotics, control, and intelligent systems

Doctoral Program in Mechanics

Doctoral Program Digital Humanities

PhD Students

Ajanic Enrico, Askari Mohammad, Bai Pu, Macchini Matteo, Piskarev Yegor, Ramachandran Vivek, Schilling Fabian Maximilian, Shin Won Dong, Soria Enrica, Vourtsis Charalampos, Wüest Valentin,

Past EPFL PhD Students

Basiri Meysam , Beyeler Antoine , Briod Adrien , Daler Ludovic , Dobrzynski Michal Karol , Dousse Nicolas , Dürr Peter , Germann Jürg Markus , Hauert Sabine , Karlen Walter , Klaptocz Adam , Kornatowski Przemyslaw Mariusz , Kovac Mirko , Lecoeur Julien Jean Denis Nicolas , Leven Severin , Lichocki Pawel , Maesani Andrea , Marbach Daniel , Mattiussi Claudio , Mitri Sara , Roberts James , Roggen Daniel , Rognon Carine , Schaffter Thomas , Shintake Jun , Stirling Timothy , Suzuki Mototaka , Urzelai Joseba , Varga Maja , Waibel Markus , Zappetti Davide , Zufferey Jean-Christophe ,

Courses

Topics in Autonomous Robotics

Students will be introduced to modern approaches in control and design of autonomous robots through lectures and exercises.

Robotics practicals

The practicals can include the following topics:

Teaching Robots to Accomplish a Manipulation Task
Experimenting with haptic interfaces
Controlling a serial robot ABB IRB 120
Control of the Micro Delta Direct Drive robot
LiniX, linear axis, assembly and control
Aerial robotics
The course provides an introduction to the design, control, and applications of aerial robots. Students will be able to translate theoretical concepts into practice by means of hands-on exercises with simulated drones.

Evolutionary robotics
The course describes theories, methods, and technologies for designing robots and artificial systems inspired by evolution, development, and learning. It also shows how robotic models can help to understand biological systems. The course ends with a discussion of future bio-hybrid robots.

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