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UID:calendar.30671.field_data.0@www.diag.uniroma1.it
DTSTAMP:20260430T094101Z
CREATED:20260421T133154Z
DESCRIPTION:Abstract:In this talk\, I will present my research on enhancing
  robotic motion and control across diverse and challenging scenarios.First
 \, I will explore the generation of optimal jumping motions using Reinforc
 ement Learning (RL)\, integrating physical insights to guide the learning 
 process.This significantly reduces the training time compared to standard 
 end-to-end approaches.Second\, I will illustrate the development of novel 
 robotic platforms that combine ropes and legged systems to address the cha
 llenges of maintenance in remote\, hazardous environments. By leveraging o
 ptimal control techniques\, I designed a computationally efficient plannin
 g algorithm for precise and adaptive jumping motions.Bio sketch:Michele Fo
 cchi is a world-recognized expert in motion planning and control of quadru
 ped robots\, with over 16 years of experience in robotics research. He is 
 currently a Professor at the University of Trento in the Department of Inf
 ormation Engineering and Computer Science (DISI)\, where he teaches roboti
 cs courses at the bachelor\, master\, and PhD levels. He is also a Scienti
 fic Advisor for industrial partners like  All3 Robotics.Prof. Focchi earne
 d both his B.Sc. and M.Sc. degrees in Control Systems Engineering from Pol
 itecnico di Milano and completed his Ph.D. in Robotics at Italian Institut
 e of Technology in 2013\, contributing to the development of the first Ita
 lian quadruped robot (HyQ). During his time at IIT\, he co-founded the Dyn
 amic Legged Systems Lab\, an internationally recognized research team focu
 sed on the development of quadruped robots and advanced locomotion strateg
 ies. His research lies at the intersection of control\, optimization\, and
  machine learning\, with a strong focus on optimization-based and Model Pr
 edictive Control techniques for robust locomotion in unstructured and chal
 lenging environments. His work has evolved from low-level locomotion contr
 ollers to whole-body control\, model identification\, and uncertainty-awar
 e motion planning for real-world applicatoms. He is particularly known for
  his pioneering contributions to heuristic locomotion strategies for quadr
 uped robots operating in rough terrain. Beyond quadruped locomotion\, Prof
 . Focchi has developed and studied innovative robotic platforms\, includin
 g rappelling robots for hydro-geological risk reduction and inspection in 
 oil & gas scenarios\, as well as control and navigation strategies for tra
 cked robots in agricultural applications. He has played leading roles in s
 everal high-profile academic and industrial projects\, including ECHORD++\
 , INAIL\, and ANT with the European Space Agency (ESA) and\, more recently
 \, EUREGIO and VRT.In 2015\, he co-founded the MOOG–IIT Joint Lab\, focuse
 d on the development of next-generation software and control technologies 
 for autonomous robots. He is the inventor and co-inventor of several paten
 ts\; notably\, in 2009 he contributed to a patented micro-turbine technolo
 gy that led to the creation of the Advanced Microturbines spin-off company
 . He has organized several scientific workshops\, including a workshop on 
 numerical optimization at Robotics: Science and Systems (RSS)\, and has de
 livered more than 25 invited talks at international conferences and worksh
 ops.He is the organizer of the first PhD summer school on Quadruped Robots
  and Dog Challenges in Italy.  He has authored or co-authored 50+ scientif
 ic publications in top international journals and conferences and has supe
 rvised numerous master’s and Ph.D. theses. He currently serves as an Assoc
 iate Editor for IEEE Robotics and Automation Letters (RA-L) and for the IE
 EE International Conference on Robotics and Automation (ICRA).
DTSTART;TZID=Europe/Paris:20260428T150000
DTEND;TZID=Europe/Paris:20260428T150000
LAST-MODIFIED:20260421T133938Z
LOCATION:Aula A6 - DIAG
SUMMARY:Avviso di Sminario 'Aerial motions for legged robots' - Prof. Miche
 le Focchi
URL;TYPE=URI:https://www.diag.uniroma1.it/node/30671
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